Create forecasting prediction engine and conform time series forecasting API to estimator standards.

docs/api-reference/io-time-series-ssa-forecast.md

@@ -0,0 +1,5 @@
+### Input and Output Columns
+There is only one input column.
+The input column must be <xref:System.Single> where a <xref:System.Single> value indicates a value at a timestamp in the time series.
+
+It produces either just one vector of forecasted values or three vectors: a vector of forecasted values, a vector of confidence lower bounds and a vector of confidence upper bounds.

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectAnomalyBySrCnn.cs

@@ -40,7 +40,7 @@ public static void Example()
  ITransformer model = ml.Transforms.DetectAnomalyBySrCnn(outputColumnName, inputColumnName, 16, 5, 5, 3, 8, 0.35).Fit(dataView);
 
  // Create a time series prediction engine from the model.
- var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, SrCnnAnomalyDetection>(ml);
+ var engine = model.CreateTimeSeriesEngine<TimeSeriesData, SrCnnAnomalyDetection>(ml);
 
  Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
  Console.WriteLine("Data\tAlert\tScore\tMag");

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsa.cs

@@ -57,7 +57,7 @@ public static void Example()
  ITransformer model = ml.Transforms.DetectChangePointBySsa(outputColumnName, inputColumnName, confidence, changeHistoryLength, TrainingSize, SeasonalitySize + 1).Fit(dataView);
 
  // Create a prediction engine from the model for feeding new data.
- var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
+ var engine = model.CreateTimeSeriesEngine<TimeSeriesData, ChangePointPrediction>(ml);
 
  // Start streaming new data points with no change point to the prediction engine.
  Console.WriteLine($"Output from ChangePoint predictions on new data:");
@@ -99,7 +99,7 @@ public static void Example()
  model = ml.Model.Load(file, out DataViewSchema schema);
 
  // We must create a new prediction engine from the persisted model.
- engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
+ engine = model.CreateTimeSeriesEngine<TimeSeriesData, ChangePointPrediction>(ml);
 
  // Run predictions on the loaded model.
  for (int i = 0; i < 5; i++)

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsaStream.cs

@@ -57,7 +57,7 @@ public static void Example()
  ITransformer model = ml.Transforms.DetectChangePointBySsa(outputColumnName, inputColumnName, confidence, changeHistoryLength, TrainingSize, SeasonalitySize + 1).Fit(dataView);
 
  // Create a prediction engine from the model for feeding new data.
- var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
+ var engine = model.CreateTimeSeriesEngine<TimeSeriesData, ChangePointPrediction>(ml);
 
  // Start streaming new data points with no change point to the prediction engine.
  Console.WriteLine($"Output from ChangePoint predictions on new data:");
@@ -103,7 +103,7 @@ public static void Example()
  model = ml.Model.Load(stream, out DataViewSchema schema);
 
  // We must create a new prediction engine from the persisted model.
- engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
+ engine = model.CreateTimeSeriesEngine<TimeSeriesData, ChangePointPrediction>(ml);
 
  // Run predictions on the loaded model.
  for (int i = 0; i < 5; i++)

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidChangePoint.cs

@@ -56,7 +56,7 @@ public static void Example()
  ITransformer model = ml.Transforms.DetectIidChangePoint(outputColumnName, inputColumnName, 95, Size / 4).Fit(dataView);
 
  // Create a time series prediction engine from the model.
- var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
+ var engine = model.CreateTimeSeriesEngine<TimeSeriesData, ChangePointPrediction>(ml);
 
  Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
  Console.WriteLine("Data\tAlert\tScore\tP-Value\tMartingale value");
@@ -97,7 +97,7 @@ public static void Example()
  model = ml.Model.Load(file, out DataViewSchema schema);
 
  // Create a time series prediction engine from the checkpointed model.
- engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
+ engine = model.CreateTimeSeriesEngine<TimeSeriesData, ChangePointPrediction>(ml);
  for (int index = 0; index < 8; index++)
  {
  // Anomaly change point detection.

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidSpike.cs

@@ -48,7 +48,7 @@ public static void Example()
  ITransformer model = ml.Transforms.DetectIidSpike(outputColumnName, inputColumnName, 95, Size).Fit(dataView);
 
  // Create a time series prediction engine from the model.
- var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, IidSpikePrediction>(ml);
+ var engine = model.CreateTimeSeriesEngine<TimeSeriesData, IidSpikePrediction>(ml);
 
  Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
  Console.WriteLine("Data\tAlert\tScore\tP-Value");

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectSpikeBySsa.cs

@@ -54,7 +54,7 @@ public static void Example()
  ITransformer model = ml.Transforms.DetectSpikeBySsa(outputColumnName, inputColumnName, 95, 8, TrainingSize, SeasonalitySize + 1).Fit(dataView);
 
  // Create a prediction engine from the model for feeding new data.
- var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, SsaSpikePrediction>(ml);
+ var engine = model.CreateTimeSeriesEngine<TimeSeriesData, SsaSpikePrediction>(ml);
 
  // Start streaming new data points with no change point to the prediction engine.
  Console.WriteLine($"Output from spike predictions on new data:");
@@ -94,7 +94,7 @@ public static void Example()
  model = ml.Model.Load(file, out DataViewSchema schema);
 
  // We must create a new prediction engine from the persisted model.
- engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, SsaSpikePrediction>(ml);
+ engine = model.CreateTimeSeriesEngine<TimeSeriesData, SsaSpikePrediction>(ml);
 
  // Run predictions on the loaded model.
  for (int i = 0; i < 5; i++)

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/Forecasting.cs

@@ -1,8 +1,8 @@
 using System;
 using System.Collections.Generic;
+using System.IO;
 using Microsoft.ML;
 using Microsoft.ML.Transforms.TimeSeries;
-using Microsoft.ML.TimeSeries;
 
 namespace Samples.Dynamic
 {
@@ -16,8 +16,7 @@ public static void Example()
  // as well as the source of randomness.
  var ml = new MLContext();
 
- // Generate sample series data with a recurring pattern
- const int SeasonalitySize = 5;
+ // Generate sample series data with a recurring pattern.
  var data = new List<TimeSeriesData>()
  {
  new TimeSeriesData(0),
@@ -44,43 +43,58 @@ public static void Example()
 
  // Setup arguments.
  var inputColumnName = nameof(TimeSeriesData.Value);
+ var outputColumnName = nameof(ForecastResult.Forecast);
 
  // Instantiate the forecasting model.
- var model = ml.Forecasting.AdaptiveSingularSpectrumSequenceModeler(inputColumnName, data.Count, SeasonalitySize + 1, SeasonalitySize,
- 1, AdaptiveSingularSpectrumSequenceModeler.RankSelectionMethod.Exact, null, SeasonalitySize / 2, false, false);
+ var model = ml.Forecasting.ForecastBySsa(outputColumnName, inputColumnName, 5, 11, data.Count, 5);
 
  // Train.
- model.Train(dataView);
+var transformer = model.Fit(dataView);
 
  // Forecast next five values.
- var forecast = model.Forecast(5);
+ var forecastEngine = transformer.CreateTimeSeriesEngine<TimeSeriesData, ForecastResult>(ml);
+ var forecast = forecastEngine.Predict();
+
  Console.WriteLine($"Forecasted values:");
- Console.WriteLine("[{0}]", string.Join(", ", forecast));
+ Console.WriteLine("[{0}]", string.Join(", ", forecast.Forecast));
  // Forecasted values:
- // [2.452744, 2.589339, 2.729183, 2.873005, 3.028931]
+ // [1.977226, 1.020494, 1.760543, 3.437509, 4.266461]
 
  // Update with new observations.
- dataView = ml.Data.LoadFromEnumerable(new List<TimeSeriesData>() { new TimeSeriesData(0), new TimeSeriesData(0), new TimeSeriesData(0), new TimeSeriesData(0) });
- model.Update(dataView);
+ forecastEngine.Predict(new TimeSeriesData(0));
+ forecastEngine.Predict(new TimeSeriesData(0));
+ forecastEngine.Predict(new TimeSeriesData(0));
+ forecastEngine.Predict(new TimeSeriesData(0));
 
  // Checkpoint.
-ml.Model.SaveForecastingModel(model, "model.zip");
+forecastEngine.CheckPoint(ml, "model.zip");
 
  // Load the checkpointed model from disk.
- var modelCopy = ml.Model.LoadForecastingModel<float>("model.zip");
+ // Load the model.
+ ITransformer modelCopy;
+ using (var file = File.OpenRead("model.zip"))
+ modelCopy = ml.Model.Load(file, out DataViewSchema schema);
+
+ // We must create a new prediction engine from the persisted model.
+ var forecastEngineCopy = modelCopy.CreateTimeSeriesEngine<TimeSeriesData, ForecastResult>(ml);
 
  // Forecast with the checkpointed model loaded from disk.
- forecast = modelCopy.Forecast(5);
- Console.WriteLine("[{0}]", string.Join(", ", forecast));
- // [0.8681176, 0.8185108, 0.8069275, 0.84405, 0.9455081]
+ forecast = forecastEngineCopy.Predict();
+ Console.WriteLine("[{0}]", string.Join(", ", forecast.Forecast));
+ // [1.791331, 1.255525, 0.3060154, -0.200446, 0.5657795]
 
  // Forecast with the original model(that was checkpointed to disk).
- forecast = model.Forecast(5);
- Console.WriteLine("[{0}]", string.Join(", ", forecast));
- // [0.8681176, 0.8185108, 0.8069275, 0.84405, 0.9455081]
+ forecast = forecastEngine.Predict();
+ Console.WriteLine("[{0}]", string.Join(", ", forecast.Forecast));
+ // [1.791331, 1.255525, 0.3060154, -0.200446, 0.5657795]
 
  }
 
+ class ForecastResult
+ {
+ public float[] Forecast { get; set; }
+ }
+
  class TimeSeriesData
  {
  public float Value;

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/ForecastingWithConfidenceInterval.cs

@@ -2,7 +2,7 @@
 using System.Collections.Generic;
 using Microsoft.ML;
 using Microsoft.ML.Transforms.TimeSeries;
-using Microsoft.ML.TimeSeries;
+using System.IO;
 
 namespace Samples.Dynamic
 {
@@ -16,8 +16,7 @@ public static void Example()
  // as well as the source of randomness.
  var ml = new MLContext();
 
- // Generate sample series data with a recurring pattern
- const int SeasonalitySize = 5;
+ // Generate sample series data with a recurring pattern.
  var data = new List<TimeSeriesData>()
  {
  new TimeSeriesData(0),
@@ -44,50 +43,58 @@ public static void Example()
 
  // Setup arguments.
  var inputColumnName = nameof(TimeSeriesData.Value);
+ var outputColumnName = nameof(ForecastResult.Forecast);
 
- // Instantiate forecasting model.
- var model = ml.Forecasting.AdaptiveSingularSpectrumSequenceModeler(inputColumnName, data.Count, SeasonalitySize + 1, SeasonalitySize,
- 1, AdaptiveSingularSpectrumSequenceModeler.RankSelectionMethod.Exact, null, SeasonalitySize / 2, shouldComputeForecastIntervals: true, false);
+ // Instantiate the forecasting model.
+ var model = ml.Forecasting.ForecastBySsa(outputColumnName, inputColumnName, 5, 11, data.Count, 5,
+ confidenceLevel: 0.95f,
+ forcastingConfidentLowerBoundColumnName: "ConfidenceLowerBound",
+ forcastingConfidentUpperBoundColumnName: "ConfidenceUpperBound");
 
  // Train.
- model.Train(dataView);
-
- // Forecast next five values with confidence internal.
- float[] forecast;
- float[] confidenceIntervalLowerBounds;
- float[] confidenceIntervalUpperBounds;
- model.ForecastWithConfidenceIntervals(5, out forecast, out confidenceIntervalLowerBounds, out confidenceIntervalUpperBounds);
- PrintForecastValuesAndIntervals(forecast, confidenceIntervalLowerBounds, confidenceIntervalUpperBounds);
+ var transformer = model.Fit(dataView);
+
+ // Forecast next five values.
+ var forecastEngine = transformer.CreateTimeSeriesEngine<TimeSeriesData, ForecastResult>(ml);
+ var forecast = forecastEngine.Predict();
+
+ PrintForecastValuesAndIntervals(forecast.Forecast, forecast.ConfidenceLowerBound, forecast.ConfidenceUpperBound);
  // Forecasted values:
- // [2.452744, 2.589339, 2.729183, 2.873005, 3.028931]
+ // [1.977226, 1.020494, 1.760543, 3.437509, 4.266461]
  // Confidence intervals:
- // [-0.2235315 - 5.12902] [-0.08777174 - 5.266451] [0.05076938 - 5.407597] [0.1925406 - 5.553469] [0.3469928 - 5.71087]
+ // [0.3451088 - 3.609343] [-0.7967533 - 2.83774] [-0.058467 - 3.579552] [1.61505 - 5.259968] [2.349299 - 6.183623]
 
  // Update with new observations.
- dataView = ml.Data.LoadFromEnumerable(new List<TimeSeriesData>() { new TimeSeriesData(0), new TimeSeriesData(0), new TimeSeriesData(0), new TimeSeriesData(0) });
- model.Update(dataView);
+ forecastEngine.Predict(new TimeSeriesData(0));
+ forecastEngine.Predict(new TimeSeriesData(0));
+ forecastEngine.Predict(new TimeSeriesData(0));
+ forecastEngine.Predict(new TimeSeriesData(0));
 
  // Checkpoint.
-ml.Model.SaveForecastingModel(model, "model.zip");
+forecastEngine.CheckPoint(ml, "model.zip");
 
  // Load the checkpointed model from disk.
- var modelCopy = ml.Model.LoadForecastingModel<float>("model.zip");
+ // Load the model.
+ ITransformer modelCopy;
+ using (var file = File.OpenRead("model.zip"))
+ modelCopy = ml.Model.Load(file, out DataViewSchema schema);
+
+ // We must create a new prediction engine from the persisted model.
+ var forecastEngineCopy = modelCopy.CreateTimeSeriesEngine<TimeSeriesData, ForecastResult>(ml);
 
  // Forecast with the checkpointed model loaded from disk.
- modelCopy.ForecastWithConfidenceIntervals(5, out forecast, out confidenceIntervalLowerBounds, out confidenceIntervalUpperBounds);
- PrintForecastValuesAndIntervals(forecast, confidenceIntervalLowerBounds, confidenceIntervalUpperBounds);
- // Forecasted values:
- // [0.8681176, 0.8185108, 0.8069275, 0.84405, 0.9455081]
+ forecast = forecastEngineCopy.Predict();
+ PrintForecastValuesAndIntervals(forecast.Forecast, forecast.ConfidenceLowerBound, forecast.ConfidenceUpperBound);
+ // [1.791331, 1.255525, 0.3060154, -0.200446, 0.5657795]
  // Confidence intervals:
- // [-1.808158 - 3.544394] [-1.8586 - 3.495622] [-1.871486 - 3.485341] [-1.836414 - 3.524514] [-1.736431 - 3.627447]
+ // [0.1592142 - 3.423448] [-0.5617217 - 3.072772] [-1.512994 - 2.125025] [-2.022905 - 1.622013] [-1.351382 - 2.482941]
 
  // Forecast with the original model(that was checkpointed to disk).
- model.ForecastWithConfidenceIntervals(5, out forecast, out confidenceIntervalLowerBounds, out confidenceIntervalUpperBounds);
- PrintForecastValuesAndIntervals(forecast, confidenceIntervalLowerBounds, confidenceIntervalUpperBounds);
- // Forecasted values:
- // [0.8681176, 0.8185108, 0.8069275, 0.84405, 0.9455081]
+ forecast = forecastEngine.Predict();
+ PrintForecastValuesAndIntervals(forecast.Forecast, forecast.ConfidenceLowerBound, forecast.ConfidenceUpperBound);
+ // [1.791331, 1.255525, 0.3060154, -0.200446, 0.5657795]
  // Confidence intervals:
- // [-1.808158 - 3.544394] [-1.8586 - 3.495622] [-1.871486 - 3.485341] [-1.836414 - 3.524514] [-1.736431 - 3.627447]
+ // [0.1592142 - 3.423448] [-0.5617217 - 3.072772] [-1.512994 - 2.125025] [-2.022905 - 1.622013] [-1.351382 - 2.482941]
  }
 
  static void PrintForecastValuesAndIntervals(float[] forecast, float[] confidenceIntervalLowerBounds, float[] confidenceIntervalUpperBounds)
@@ -100,6 +107,13 @@ static void PrintForecastValuesAndIntervals(float[] forecast, float[] confidence
  Console.WriteLine();
  }
 
+ class ForecastResult
+ {
+ public float[] Forecast { get; set; }
+ public float[] ConfidenceLowerBound { get; set; }
+ public float[] ConfidenceUpperBound { get; set; }
+ }
+
  class TimeSeriesData
  {
  public float Value;

docs/samples/Microsoft.ML.Samples/Program.cs

@@ -1,5 +1,6 @@
 using System;
 using System.Reflection;
+using Samples.Dynamic;
 
 namespace Microsoft.ML.Samples
 {

docs/api-reference/io-time-series-ssa-forecast.md

@@ -0,0 +1,5 @@
+### Input and Output Columns
+There is only one input column.
+The input column must be <xref:System.Single> where a <xref:System.Single> value indicates a value at a timestamp in the time series.
+
+It produces either just one vector of forecasted values or three vectors: a vector of forecasted values, a vector of confidence lower bounds and a vector of confidence upper bounds.

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectAnomalyBySrCnn.cs

@@ -40,7 +40,7 @@ public static void Example()
  ITransformer model = ml.Transforms.DetectAnomalyBySrCnn(outputColumnName, inputColumnName, 16, 5, 5, 3, 8, 0.35).Fit(dataView);
 
  // Create a time series prediction engine from the model.
- var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, SrCnnAnomalyDetection>(ml);
+ var engine = model.CreateTimeSeriesEngine<TimeSeriesData, SrCnnAnomalyDetection>(ml);
 
  Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
  Console.WriteLine("Data\tAlert\tScore\tMag");

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsa.cs

@@ -57,7 +57,7 @@ public static void Example()
  ITransformer model = ml.Transforms.DetectChangePointBySsa(outputColumnName, inputColumnName, confidence, changeHistoryLength, TrainingSize, SeasonalitySize + 1).Fit(dataView);
 
  // Create a prediction engine from the model for feeding new data.
- var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
+ var engine = model.CreateTimeSeriesEngine<TimeSeriesData, ChangePointPrediction>(ml);
 
  // Start streaming new data points with no change point to the prediction engine.
  Console.WriteLine($"Output from ChangePoint predictions on new data:");
@@ -99,7 +99,7 @@ public static void Example()
  model = ml.Model.Load(file, out DataViewSchema schema);
 
  // We must create a new prediction engine from the persisted model.
- engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
+ engine = model.CreateTimeSeriesEngine<TimeSeriesData, ChangePointPrediction>(ml);
 
  // Run predictions on the loaded model.
  for (int i = 0; i < 5; i++)

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsaStream.cs

@@ -57,7 +57,7 @@ public static void Example()
  ITransformer model = ml.Transforms.DetectChangePointBySsa(outputColumnName, inputColumnName, confidence, changeHistoryLength, TrainingSize, SeasonalitySize + 1).Fit(dataView);
 
  // Create a prediction engine from the model for feeding new data.
- var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
+ var engine = model.CreateTimeSeriesEngine<TimeSeriesData, ChangePointPrediction>(ml);
 
  // Start streaming new data points with no change point to the prediction engine.
  Console.WriteLine($"Output from ChangePoint predictions on new data:");
@@ -103,7 +103,7 @@ public static void Example()
  model = ml.Model.Load(stream, out DataViewSchema schema);
 
  // We must create a new prediction engine from the persisted model.
- engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
+ engine = model.CreateTimeSeriesEngine<TimeSeriesData, ChangePointPrediction>(ml);
 
  // Run predictions on the loaded model.
  for (int i = 0; i < 5; i++)

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidChangePoint.cs

@@ -56,7 +56,7 @@ public static void Example()
  ITransformer model = ml.Transforms.DetectIidChangePoint(outputColumnName, inputColumnName, 95, Size / 4).Fit(dataView);
 
  // Create a time series prediction engine from the model.
- var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
+ var engine = model.CreateTimeSeriesEngine<TimeSeriesData, ChangePointPrediction>(ml);
 
  Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
  Console.WriteLine("Data\tAlert\tScore\tP-Value\tMartingale value");
@@ -97,7 +97,7 @@ public static void Example()
  model = ml.Model.Load(file, out DataViewSchema schema);
 
  // Create a time series prediction engine from the checkpointed model.
- engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
+ engine = model.CreateTimeSeriesEngine<TimeSeriesData, ChangePointPrediction>(ml);
  for (int index = 0; index < 8; index++)
  {
  // Anomaly change point detection.

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidSpike.cs

@@ -48,7 +48,7 @@ public static void Example()
  ITransformer model = ml.Transforms.DetectIidSpike(outputColumnName, inputColumnName, 95, Size).Fit(dataView);
 
  // Create a time series prediction engine from the model.
- var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, IidSpikePrediction>(ml);
+ var engine = model.CreateTimeSeriesEngine<TimeSeriesData, IidSpikePrediction>(ml);
 
  Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
  Console.WriteLine("Data\tAlert\tScore\tP-Value");

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectSpikeBySsa.cs

@@ -54,7 +54,7 @@ public static void Example()
  ITransformer model = ml.Transforms.DetectSpikeBySsa(outputColumnName, inputColumnName, 95, 8, TrainingSize, SeasonalitySize + 1).Fit(dataView);
 
  // Create a prediction engine from the model for feeding new data.
- var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, SsaSpikePrediction>(ml);
+ var engine = model.CreateTimeSeriesEngine<TimeSeriesData, SsaSpikePrediction>(ml);
 
  // Start streaming new data points with no change point to the prediction engine.
  Console.WriteLine($"Output from spike predictions on new data:");
@@ -94,7 +94,7 @@ public static void Example()
  model = ml.Model.Load(file, out DataViewSchema schema);
 
  // We must create a new prediction engine from the persisted model.
- engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, SsaSpikePrediction>(ml);
+ engine = model.CreateTimeSeriesEngine<TimeSeriesData, SsaSpikePrediction>(ml);
 
  // Run predictions on the loaded model.
  for (int i = 0; i < 5; i++)

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/Forecasting.cs

@@ -1,8 +1,8 @@
 using System;
 using System.Collections.Generic;
+using System.IO;
 using Microsoft.ML;
 using Microsoft.ML.Transforms.TimeSeries;
-using Microsoft.ML.TimeSeries;
 
 namespace Samples.Dynamic
 {
@@ -16,8 +16,7 @@ public static void Example()
  // as well as the source of randomness.
  var ml = new MLContext();
 
- // Generate sample series data with a recurring pattern
- const int SeasonalitySize = 5;
+ // Generate sample series data with a recurring pattern.
  var data = new List<TimeSeriesData>()
  {
  new TimeSeriesData(0),
@@ -44,43 +43,58 @@ public static void Example()
 
  // Setup arguments.
  var inputColumnName = nameof(TimeSeriesData.Value);
+ var outputColumnName = nameof(ForecastResult.Forecast);
 
  // Instantiate the forecasting model.
- var model = ml.Forecasting.AdaptiveSingularSpectrumSequenceModeler(inputColumnName, data.Count, SeasonalitySize + 1, SeasonalitySize,
- 1, AdaptiveSingularSpectrumSequenceModeler.RankSelectionMethod.Exact, null, SeasonalitySize / 2, false, false);
+ var model = ml.Forecasting.ForecastBySsa(outputColumnName, inputColumnName, 5, 11, data.Count, 5);
 
  // Train.
- model.Train(dataView);
+var transformer = model.Fit(dataView);
 
  // Forecast next five values.
- var forecast = model.Forecast(5);
+ var forecastEngine = transformer.CreateTimeSeriesEngine<TimeSeriesData, ForecastResult>(ml);
+ var forecast = forecastEngine.Predict();
+
  Console.WriteLine($"Forecasted values:");
- Console.WriteLine("[{0}]", string.Join(", ", forecast));
+ Console.WriteLine("[{0}]", string.Join(", ", forecast.Forecast));
  // Forecasted values:
- // [2.452744, 2.589339, 2.729183, 2.873005, 3.028931]
+ // [1.977226, 1.020494, 1.760543, 3.437509, 4.266461]
 
  // Update with new observations.
- dataView = ml.Data.LoadFromEnumerable(new List<TimeSeriesData>() { new TimeSeriesData(0), new TimeSeriesData(0), new TimeSeriesData(0), new TimeSeriesData(0) });
- model.Update(dataView);
+ forecastEngine.Predict(new TimeSeriesData(0));
+ forecastEngine.Predict(new TimeSeriesData(0));
+ forecastEngine.Predict(new TimeSeriesData(0));
+ forecastEngine.Predict(new TimeSeriesData(0));
 
  // Checkpoint.
-ml.Model.SaveForecastingModel(model, "model.zip");
+forecastEngine.CheckPoint(ml, "model.zip");
 
  // Load the checkpointed model from disk.
- var modelCopy = ml.Model.LoadForecastingModel<float>("model.zip");
+ // Load the model.
+ ITransformer modelCopy;
+ using (var file = File.OpenRead("model.zip"))
+ modelCopy = ml.Model.Load(file, out DataViewSchema schema);
+
+ // We must create a new prediction engine from the persisted model.
+ var forecastEngineCopy = modelCopy.CreateTimeSeriesEngine<TimeSeriesData, ForecastResult>(ml);
 
  // Forecast with the checkpointed model loaded from disk.
- forecast = modelCopy.Forecast(5);
- Console.WriteLine("[{0}]", string.Join(", ", forecast));
- // [0.8681176, 0.8185108, 0.8069275, 0.84405, 0.9455081]
+ forecast = forecastEngineCopy.Predict();
+ Console.WriteLine("[{0}]", string.Join(", ", forecast.Forecast));
+ // [1.791331, 1.255525, 0.3060154, -0.200446, 0.5657795]
 
  // Forecast with the original model(that was checkpointed to disk).
- forecast = model.Forecast(5);
- Console.WriteLine("[{0}]", string.Join(", ", forecast));
- // [0.8681176, 0.8185108, 0.8069275, 0.84405, 0.9455081]
+ forecast = forecastEngine.Predict();
+ Console.WriteLine("[{0}]", string.Join(", ", forecast.Forecast));
+ // [1.791331, 1.255525, 0.3060154, -0.200446, 0.5657795]
 
  }
 
+ class ForecastResult
+ {
+ public float[] Forecast { get; set; }
+ }
+
  class TimeSeriesData
  {
  public float Value;

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/ForecastingWithConfidenceInterval.cs

@@ -2,7 +2,7 @@
 using System.Collections.Generic;
 using Microsoft.ML;
 using Microsoft.ML.Transforms.TimeSeries;
-using Microsoft.ML.TimeSeries;
+using System.IO;
 
 namespace Samples.Dynamic
 {
@@ -16,8 +16,7 @@ public static void Example()
  // as well as the source of randomness.
  var ml = new MLContext();
 
- // Generate sample series data with a recurring pattern
- const int SeasonalitySize = 5;
+ // Generate sample series data with a recurring pattern.
  var data = new List<TimeSeriesData>()
  {
  new TimeSeriesData(0),
@@ -44,50 +43,58 @@ public static void Example()
 
  // Setup arguments.
  var inputColumnName = nameof(TimeSeriesData.Value);
+ var outputColumnName = nameof(ForecastResult.Forecast);
 
- // Instantiate forecasting model.
- var model = ml.Forecasting.AdaptiveSingularSpectrumSequenceModeler(inputColumnName, data.Count, SeasonalitySize + 1, SeasonalitySize,
- 1, AdaptiveSingularSpectrumSequenceModeler.RankSelectionMethod.Exact, null, SeasonalitySize / 2, shouldComputeForecastIntervals: true, false);
+ // Instantiate the forecasting model.
+ var model = ml.Forecasting.ForecastBySsa(outputColumnName, inputColumnName, 5, 11, data.Count, 5,
+ confidenceLevel: 0.95f,
+ forcastingConfidentLowerBoundColumnName: "ConfidenceLowerBound",
+ forcastingConfidentUpperBoundColumnName: "ConfidenceUpperBound");
 
  // Train.
- model.Train(dataView);
-
- // Forecast next five values with confidence internal.
- float[] forecast;
- float[] confidenceIntervalLowerBounds;
- float[] confidenceIntervalUpperBounds;
- model.ForecastWithConfidenceIntervals(5, out forecast, out confidenceIntervalLowerBounds, out confidenceIntervalUpperBounds);
- PrintForecastValuesAndIntervals(forecast, confidenceIntervalLowerBounds, confidenceIntervalUpperBounds);
+ var transformer = model.Fit(dataView);
+
+ // Forecast next five values.
+ var forecastEngine = transformer.CreateTimeSeriesEngine<TimeSeriesData, ForecastResult>(ml);
+ var forecast = forecastEngine.Predict();
+
+ PrintForecastValuesAndIntervals(forecast.Forecast, forecast.ConfidenceLowerBound, forecast.ConfidenceUpperBound);
  // Forecasted values:
- // [2.452744, 2.589339, 2.729183, 2.873005, 3.028931]
+ // [1.977226, 1.020494, 1.760543, 3.437509, 4.266461]
  // Confidence intervals:
- // [-0.2235315 - 5.12902] [-0.08777174 - 5.266451] [0.05076938 - 5.407597] [0.1925406 - 5.553469] [0.3469928 - 5.71087]
+ // [0.3451088 - 3.609343] [-0.7967533 - 2.83774] [-0.058467 - 3.579552] [1.61505 - 5.259968] [2.349299 - 6.183623]
 
  // Update with new observations.
- dataView = ml.Data.LoadFromEnumerable(new List<TimeSeriesData>() { new TimeSeriesData(0), new TimeSeriesData(0), new TimeSeriesData(0), new TimeSeriesData(0) });
- model.Update(dataView);
+ forecastEngine.Predict(new TimeSeriesData(0));
+ forecastEngine.Predict(new TimeSeriesData(0));
+ forecastEngine.Predict(new TimeSeriesData(0));
+ forecastEngine.Predict(new TimeSeriesData(0));
 
  // Checkpoint.
-ml.Model.SaveForecastingModel(model, "model.zip");
+forecastEngine.CheckPoint(ml, "model.zip");
 
  // Load the checkpointed model from disk.
- var modelCopy = ml.Model.LoadForecastingModel<float>("model.zip");
+ // Load the model.
+ ITransformer modelCopy;
+ using (var file = File.OpenRead("model.zip"))
+ modelCopy = ml.Model.Load(file, out DataViewSchema schema);
+
+ // We must create a new prediction engine from the persisted model.
+ var forecastEngineCopy = modelCopy.CreateTimeSeriesEngine<TimeSeriesData, ForecastResult>(ml);
 
  // Forecast with the checkpointed model loaded from disk.
- modelCopy.ForecastWithConfidenceIntervals(5, out forecast, out confidenceIntervalLowerBounds, out confidenceIntervalUpperBounds);
- PrintForecastValuesAndIntervals(forecast, confidenceIntervalLowerBounds, confidenceIntervalUpperBounds);
- // Forecasted values:
- // [0.8681176, 0.8185108, 0.8069275, 0.84405, 0.9455081]
+ forecast = forecastEngineCopy.Predict();
+ PrintForecastValuesAndIntervals(forecast.Forecast, forecast.ConfidenceLowerBound, forecast.ConfidenceUpperBound);
+ // [1.791331, 1.255525, 0.3060154, -0.200446, 0.5657795]
  // Confidence intervals:
- // [-1.808158 - 3.544394] [-1.8586 - 3.495622] [-1.871486 - 3.485341] [-1.836414 - 3.524514] [-1.736431 - 3.627447]
+ // [0.1592142 - 3.423448] [-0.5617217 - 3.072772] [-1.512994 - 2.125025] [-2.022905 - 1.622013] [-1.351382 - 2.482941]
 
  // Forecast with the original model(that was checkpointed to disk).
- model.ForecastWithConfidenceIntervals(5, out forecast, out confidenceIntervalLowerBounds, out confidenceIntervalUpperBounds);
- PrintForecastValuesAndIntervals(forecast, confidenceIntervalLowerBounds, confidenceIntervalUpperBounds);
- // Forecasted values:
- // [0.8681176, 0.8185108, 0.8069275, 0.84405, 0.9455081]
+ forecast = forecastEngine.Predict();
+ PrintForecastValuesAndIntervals(forecast.Forecast, forecast.ConfidenceLowerBound, forecast.ConfidenceUpperBound);
+ // [1.791331, 1.255525, 0.3060154, -0.200446, 0.5657795]
  // Confidence intervals:
- // [-1.808158 - 3.544394] [-1.8586 - 3.495622] [-1.871486 - 3.485341] [-1.836414 - 3.524514] [-1.736431 - 3.627447]
+ // [0.1592142 - 3.423448] [-0.5617217 - 3.072772] [-1.512994 - 2.125025] [-2.022905 - 1.622013] [-1.351382 - 2.482941]
  }
 
  static void PrintForecastValuesAndIntervals(float[] forecast, float[] confidenceIntervalLowerBounds, float[] confidenceIntervalUpperBounds)
@@ -100,6 +107,13 @@ static void PrintForecastValuesAndIntervals(float[] forecast, float[] confidence
  Console.WriteLine();
  }
 
+ class ForecastResult
+ {
+ public float[] Forecast { get; set; }
+ public float[] ConfidenceLowerBound { get; set; }
+ public float[] ConfidenceUpperBound { get; set; }
+ }
+
  class TimeSeriesData
  {
  public float Value;

docs/samples/Microsoft.ML.Samples/Program.cs

@@ -1,5 +1,6 @@
 using System;
 using System.Reflection;
+using Samples.Dynamic;
 
 namespace Microsoft.ML.Samples
 {

docs/api-reference/io-time-series-ssa-forecast.md

@@ -0,0 +1,5 @@
+### Input and Output Columns
+There is only one input column.
+The input column must be <xref:System.Single> where a <xref:System.Single> value indicates a value at a timestamp in the time series.
+
+It produces either just one vector of forecasted values or three vectors: a vector of forecasted values, a vector of confidence lower bounds and a vector of confidence upper bounds.

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectAnomalyBySrCnn.cs

@@ -40,7 +40,7 @@ public static void Example()
  ITransformer model = ml.Transforms.DetectAnomalyBySrCnn(outputColumnName, inputColumnName, 16, 5, 5, 3, 8, 0.35).Fit(dataView);
 
  // Create a time series prediction engine from the model.
- var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, SrCnnAnomalyDetection>(ml);
+ var engine = model.CreateTimeSeriesEngine<TimeSeriesData, SrCnnAnomalyDetection>(ml);
 
  Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
  Console.WriteLine("Data\tAlert\tScore\tMag");

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsa.cs

@@ -57,7 +57,7 @@ public static void Example()
  ITransformer model = ml.Transforms.DetectChangePointBySsa(outputColumnName, inputColumnName, confidence, changeHistoryLength, TrainingSize, SeasonalitySize + 1).Fit(dataView);
 
  // Create a prediction engine from the model for feeding new data.
- var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
+ var engine = model.CreateTimeSeriesEngine<TimeSeriesData, ChangePointPrediction>(ml);
 
  // Start streaming new data points with no change point to the prediction engine.
  Console.WriteLine($"Output from ChangePoint predictions on new data:");
@@ -99,7 +99,7 @@ public static void Example()
  model = ml.Model.Load(file, out DataViewSchema schema);
 
  // We must create a new prediction engine from the persisted model.
- engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
+ engine = model.CreateTimeSeriesEngine<TimeSeriesData, ChangePointPrediction>(ml);
 
  // Run predictions on the loaded model.
  for (int i = 0; i < 5; i++)

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsaStream.cs

@@ -57,7 +57,7 @@ public static void Example()
  ITransformer model = ml.Transforms.DetectChangePointBySsa(outputColumnName, inputColumnName, confidence, changeHistoryLength, TrainingSize, SeasonalitySize + 1).Fit(dataView);
 
  // Create a prediction engine from the model for feeding new data.
- var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
+ var engine = model.CreateTimeSeriesEngine<TimeSeriesData, ChangePointPrediction>(ml);
 
  // Start streaming new data points with no change point to the prediction engine.
  Console.WriteLine($"Output from ChangePoint predictions on new data:");
@@ -103,7 +103,7 @@ public static void Example()
  model = ml.Model.Load(stream, out DataViewSchema schema);
 
  // We must create a new prediction engine from the persisted model.
- engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
+ engine = model.CreateTimeSeriesEngine<TimeSeriesData, ChangePointPrediction>(ml);
 
  // Run predictions on the loaded model.
  for (int i = 0; i < 5; i++)

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidChangePoint.cs

@@ -56,7 +56,7 @@ public static void Example()
  ITransformer model = ml.Transforms.DetectIidChangePoint(outputColumnName, inputColumnName, 95, Size / 4).Fit(dataView);
 
  // Create a time series prediction engine from the model.
- var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
+ var engine = model.CreateTimeSeriesEngine<TimeSeriesData, ChangePointPrediction>(ml);
 
  Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
  Console.WriteLine("Data\tAlert\tScore\tP-Value\tMartingale value");
@@ -97,7 +97,7 @@ public static void Example()
  model = ml.Model.Load(file, out DataViewSchema schema);
 
  // Create a time series prediction engine from the checkpointed model.
- engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
+ engine = model.CreateTimeSeriesEngine<TimeSeriesData, ChangePointPrediction>(ml);
  for (int index = 0; index < 8; index++)
  {
  // Anomaly change point detection.

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidSpike.cs

@@ -48,7 +48,7 @@ public static void Example()
  ITransformer model = ml.Transforms.DetectIidSpike(outputColumnName, inputColumnName, 95, Size).Fit(dataView);
 
  // Create a time series prediction engine from the model.
- var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, IidSpikePrediction>(ml);
+ var engine = model.CreateTimeSeriesEngine<TimeSeriesData, IidSpikePrediction>(ml);
 
  Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
  Console.WriteLine("Data\tAlert\tScore\tP-Value");

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectSpikeBySsa.cs

@@ -54,7 +54,7 @@ public static void Example()
  ITransformer model = ml.Transforms.DetectSpikeBySsa(outputColumnName, inputColumnName, 95, 8, TrainingSize, SeasonalitySize + 1).Fit(dataView);
 
  // Create a prediction engine from the model for feeding new data.
- var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, SsaSpikePrediction>(ml);
+ var engine = model.CreateTimeSeriesEngine<TimeSeriesData, SsaSpikePrediction>(ml);
 
  // Start streaming new data points with no change point to the prediction engine.
  Console.WriteLine($"Output from spike predictions on new data:");
@@ -94,7 +94,7 @@ public static void Example()
  model = ml.Model.Load(file, out DataViewSchema schema);
 
  // We must create a new prediction engine from the persisted model.
- engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, SsaSpikePrediction>(ml);
+ engine = model.CreateTimeSeriesEngine<TimeSeriesData, SsaSpikePrediction>(ml);
 
  // Run predictions on the loaded model.
  for (int i = 0; i < 5; i++)

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/Forecasting.cs

@@ -1,8 +1,8 @@
 using System;
 using System.Collections.Generic;
+using System.IO;
 using Microsoft.ML;
 using Microsoft.ML.Transforms.TimeSeries;
-using Microsoft.ML.TimeSeries;
 
 namespace Samples.Dynamic
 {
@@ -16,8 +16,7 @@ public static void Example()
  // as well as the source of randomness.
  var ml = new MLContext();
 
- // Generate sample series data with a recurring pattern
- const int SeasonalitySize = 5;
+ // Generate sample series data with a recurring pattern.
  var data = new List<TimeSeriesData>()
  {
  new TimeSeriesData(0),
@@ -44,43 +43,58 @@ public static void Example()
 
  // Setup arguments.
  var inputColumnName = nameof(TimeSeriesData.Value);
+ var outputColumnName = nameof(ForecastResult.Forecast);
 
  // Instantiate the forecasting model.
- var model = ml.Forecasting.AdaptiveSingularSpectrumSequenceModeler(inputColumnName, data.Count, SeasonalitySize + 1, SeasonalitySize,
- 1, AdaptiveSingularSpectrumSequenceModeler.RankSelectionMethod.Exact, null, SeasonalitySize / 2, false, false);
+ var model = ml.Forecasting.ForecastBySsa(outputColumnName, inputColumnName, 5, 11, data.Count, 5);
 
  // Train.
- model.Train(dataView);
+var transformer = model.Fit(dataView);
 
  // Forecast next five values.
- var forecast = model.Forecast(5);
+ var forecastEngine = transformer.CreateTimeSeriesEngine<TimeSeriesData, ForecastResult>(ml);
+ var forecast = forecastEngine.Predict();
+
  Console.WriteLine($"Forecasted values:");
- Console.WriteLine("[{0}]", string.Join(", ", forecast));
+ Console.WriteLine("[{0}]", string.Join(", ", forecast.Forecast));
  // Forecasted values:
- // [2.452744, 2.589339, 2.729183, 2.873005, 3.028931]
+ // [1.977226, 1.020494, 1.760543, 3.437509, 4.266461]
 
  // Update with new observations.
- dataView = ml.Data.LoadFromEnumerable(new List<TimeSeriesData>() { new TimeSeriesData(0), new TimeSeriesData(0), new TimeSeriesData(0), new TimeSeriesData(0) });
- model.Update(dataView);
+ forecastEngine.Predict(new TimeSeriesData(0));
+ forecastEngine.Predict(new TimeSeriesData(0));
+ forecastEngine.Predict(new TimeSeriesData(0));
+ forecastEngine.Predict(new TimeSeriesData(0));
 
  // Checkpoint.
-ml.Model.SaveForecastingModel(model, "model.zip");
+forecastEngine.CheckPoint(ml, "model.zip");
 
  // Load the checkpointed model from disk.
- var modelCopy = ml.Model.LoadForecastingModel<float>("model.zip");
+ // Load the model.
+ ITransformer modelCopy;
+ using (var file = File.OpenRead("model.zip"))
+ modelCopy = ml.Model.Load(file, out DataViewSchema schema);
+
+ // We must create a new prediction engine from the persisted model.
+ var forecastEngineCopy = modelCopy.CreateTimeSeriesEngine<TimeSeriesData, ForecastResult>(ml);
 
  // Forecast with the checkpointed model loaded from disk.
- forecast = modelCopy.Forecast(5);
- Console.WriteLine("[{0}]", string.Join(", ", forecast));
- // [0.8681176, 0.8185108, 0.8069275, 0.84405, 0.9455081]
+ forecast = forecastEngineCopy.Predict();
+ Console.WriteLine("[{0}]", string.Join(", ", forecast.Forecast));
+ // [1.791331, 1.255525, 0.3060154, -0.200446, 0.5657795]
 
  // Forecast with the original model(that was checkpointed to disk).
- forecast = model.Forecast(5);
- Console.WriteLine("[{0}]", string.Join(", ", forecast));
- // [0.8681176, 0.8185108, 0.8069275, 0.84405, 0.9455081]
+ forecast = forecastEngine.Predict();
+ Console.WriteLine("[{0}]", string.Join(", ", forecast.Forecast));
+ // [1.791331, 1.255525, 0.3060154, -0.200446, 0.5657795]
 
  }
 
+ class ForecastResult
+ {
+ public float[] Forecast { get; set; }
+ }
+
  class TimeSeriesData
  {
  public float Value;

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/ForecastingWithConfidenceInterval.cs

@@ -2,7 +2,7 @@
 using System.Collections.Generic;
 using Microsoft.ML;
 using Microsoft.ML.Transforms.TimeSeries;
-using Microsoft.ML.TimeSeries;
+using System.IO;
 
 namespace Samples.Dynamic
 {
@@ -16,8 +16,7 @@ public static void Example()
  // as well as the source of randomness.
  var ml = new MLContext();
 
- // Generate sample series data with a recurring pattern
- const int SeasonalitySize = 5;
+ // Generate sample series data with a recurring pattern.
  var data = new List<TimeSeriesData>()
  {
  new TimeSeriesData(0),
@@ -44,50 +43,58 @@ public static void Example()
 
  // Setup arguments.
  var inputColumnName = nameof(TimeSeriesData.Value);
+ var outputColumnName = nameof(ForecastResult.Forecast);
 
- // Instantiate forecasting model.
- var model = ml.Forecasting.AdaptiveSingularSpectrumSequenceModeler(inputColumnName, data.Count, SeasonalitySize + 1, SeasonalitySize,
- 1, AdaptiveSingularSpectrumSequenceModeler.RankSelectionMethod.Exact, null, SeasonalitySize / 2, shouldComputeForecastIntervals: true, false);
+ // Instantiate the forecasting model.
+ var model = ml.Forecasting.ForecastBySsa(outputColumnName, inputColumnName, 5, 11, data.Count, 5,
+ confidenceLevel: 0.95f,
+ forcastingConfidentLowerBoundColumnName: "ConfidenceLowerBound",
+ forcastingConfidentUpperBoundColumnName: "ConfidenceUpperBound");
 
  // Train.
- model.Train(dataView);
-
- // Forecast next five values with confidence internal.
- float[] forecast;
- float[] confidenceIntervalLowerBounds;
- float[] confidenceIntervalUpperBounds;
- model.ForecastWithConfidenceIntervals(5, out forecast, out confidenceIntervalLowerBounds, out confidenceIntervalUpperBounds);
- PrintForecastValuesAndIntervals(forecast, confidenceIntervalLowerBounds, confidenceIntervalUpperBounds);
+ var transformer = model.Fit(dataView);
+
+ // Forecast next five values.
+ var forecastEngine = transformer.CreateTimeSeriesEngine<TimeSeriesData, ForecastResult>(ml);
+ var forecast = forecastEngine.Predict();
+
+ PrintForecastValuesAndIntervals(forecast.Forecast, forecast.ConfidenceLowerBound, forecast.ConfidenceUpperBound);
  // Forecasted values:
- // [2.452744, 2.589339, 2.729183, 2.873005, 3.028931]
+ // [1.977226, 1.020494, 1.760543, 3.437509, 4.266461]
  // Confidence intervals:
- // [-0.2235315 - 5.12902] [-0.08777174 - 5.266451] [0.05076938 - 5.407597] [0.1925406 - 5.553469] [0.3469928 - 5.71087]
+ // [0.3451088 - 3.609343] [-0.7967533 - 2.83774] [-0.058467 - 3.579552] [1.61505 - 5.259968] [2.349299 - 6.183623]
 
  // Update with new observations.
- dataView = ml.Data.LoadFromEnumerable(new List<TimeSeriesData>() { new TimeSeriesData(0), new TimeSeriesData(0), new TimeSeriesData(0), new TimeSeriesData(0) });
- model.Update(dataView);
+ forecastEngine.Predict(new TimeSeriesData(0));
+ forecastEngine.Predict(new TimeSeriesData(0));
+ forecastEngine.Predict(new TimeSeriesData(0));
+ forecastEngine.Predict(new TimeSeriesData(0));
 
  // Checkpoint.
-ml.Model.SaveForecastingModel(model, "model.zip");
+forecastEngine.CheckPoint(ml, "model.zip");
 
  // Load the checkpointed model from disk.
- var modelCopy = ml.Model.LoadForecastingModel<float>("model.zip");
+ // Load the model.
+ ITransformer modelCopy;
+ using (var file = File.OpenRead("model.zip"))
+ modelCopy = ml.Model.Load(file, out DataViewSchema schema);
+
+ // We must create a new prediction engine from the persisted model.
+ var forecastEngineCopy = modelCopy.CreateTimeSeriesEngine<TimeSeriesData, ForecastResult>(ml);
 
  // Forecast with the checkpointed model loaded from disk.
- modelCopy.ForecastWithConfidenceIntervals(5, out forecast, out confidenceIntervalLowerBounds, out confidenceIntervalUpperBounds);
- PrintForecastValuesAndIntervals(forecast, confidenceIntervalLowerBounds, confidenceIntervalUpperBounds);
- // Forecasted values:
- // [0.8681176, 0.8185108, 0.8069275, 0.84405, 0.9455081]
+ forecast = forecastEngineCopy.Predict();
+ PrintForecastValuesAndIntervals(forecast.Forecast, forecast.ConfidenceLowerBound, forecast.ConfidenceUpperBound);
+ // [1.791331, 1.255525, 0.3060154, -0.200446, 0.5657795]
  // Confidence intervals:
- // [-1.808158 - 3.544394] [-1.8586 - 3.495622] [-1.871486 - 3.485341] [-1.836414 - 3.524514] [-1.736431 - 3.627447]
+ // [0.1592142 - 3.423448] [-0.5617217 - 3.072772] [-1.512994 - 2.125025] [-2.022905 - 1.622013] [-1.351382 - 2.482941]
 
  // Forecast with the original model(that was checkpointed to disk).
- model.ForecastWithConfidenceIntervals(5, out forecast, out confidenceIntervalLowerBounds, out confidenceIntervalUpperBounds);
- PrintForecastValuesAndIntervals(forecast, confidenceIntervalLowerBounds, confidenceIntervalUpperBounds);
- // Forecasted values:
- // [0.8681176, 0.8185108, 0.8069275, 0.84405, 0.9455081]
+ forecast = forecastEngine.Predict();
+ PrintForecastValuesAndIntervals(forecast.Forecast, forecast.ConfidenceLowerBound, forecast.ConfidenceUpperBound);
+ // [1.791331, 1.255525, 0.3060154, -0.200446, 0.5657795]
  // Confidence intervals:
- // [-1.808158 - 3.544394] [-1.8586 - 3.495622] [-1.871486 - 3.485341] [-1.836414 - 3.524514] [-1.736431 - 3.627447]
+ // [0.1592142 - 3.423448] [-0.5617217 - 3.072772] [-1.512994 - 2.125025] [-2.022905 - 1.622013] [-1.351382 - 2.482941]
  }
 
  static void PrintForecastValuesAndIntervals(float[] forecast, float[] confidenceIntervalLowerBounds, float[] confidenceIntervalUpperBounds)
@@ -100,6 +107,13 @@ static void PrintForecastValuesAndIntervals(float[] forecast, float[] confidence
  Console.WriteLine();
  }
 
+ class ForecastResult
+ {
+ public float[] Forecast { get; set; }
+ public float[] ConfidenceLowerBound { get; set; }
+ public float[] ConfidenceUpperBound { get; set; }
+ }
+
  class TimeSeriesData
  {
  public float Value;

docs/samples/Microsoft.ML.Samples/Program.cs

@@ -1,5 +1,6 @@
 using System;
 using System.Reflection;
+using Samples.Dynamic;
 
 namespace Microsoft.ML.Samples
 {

docs/api-reference/io-time-series-ssa-forecast.md

@@ -0,0 +1,5 @@
+### Input and Output Columns
+There is only one input column.
+The input column must be <xref:System.Single> where a <xref:System.Single> value indicates a value at a timestamp in the time series.
+
+It produces either just one vector of forecasted values or three vectors: a vector of forecasted values, a vector of confidence lower bounds and a vector of confidence upper bounds.

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectAnomalyBySrCnn.cs

@@ -40,7 +40,7 @@ public static void Example()
  ITransformer model = ml.Transforms.DetectAnomalyBySrCnn(outputColumnName, inputColumnName, 16, 5, 5, 3, 8, 0.35).Fit(dataView);
 
  // Create a time series prediction engine from the model.
- var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, SrCnnAnomalyDetection>(ml);
+ var engine = model.CreateTimeSeriesEngine<TimeSeriesData, SrCnnAnomalyDetection>(ml);
 
  Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
  Console.WriteLine("Data\tAlert\tScore\tMag");

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsa.cs

@@ -57,7 +57,7 @@ public static void Example()
  ITransformer model = ml.Transforms.DetectChangePointBySsa(outputColumnName, inputColumnName, confidence, changeHistoryLength, TrainingSize, SeasonalitySize + 1).Fit(dataView);
 
  // Create a prediction engine from the model for feeding new data.
- var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
+ var engine = model.CreateTimeSeriesEngine<TimeSeriesData, ChangePointPrediction>(ml);
 
  // Start streaming new data points with no change point to the prediction engine.
  Console.WriteLine($"Output from ChangePoint predictions on new data:");
@@ -99,7 +99,7 @@ public static void Example()
  model = ml.Model.Load(file, out DataViewSchema schema);
 
  // We must create a new prediction engine from the persisted model.
- engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
+ engine = model.CreateTimeSeriesEngine<TimeSeriesData, ChangePointPrediction>(ml);
 
  // Run predictions on the loaded model.
  for (int i = 0; i < 5; i++)

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsaStream.cs

@@ -57,7 +57,7 @@ public static void Example()
  ITransformer model = ml.Transforms.DetectChangePointBySsa(outputColumnName, inputColumnName, confidence, changeHistoryLength, TrainingSize, SeasonalitySize + 1).Fit(dataView);
 
  // Create a prediction engine from the model for feeding new data.
- var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
+ var engine = model.CreateTimeSeriesEngine<TimeSeriesData, ChangePointPrediction>(ml);
 
  // Start streaming new data points with no change point to the prediction engine.
  Console.WriteLine($"Output from ChangePoint predictions on new data:");
@@ -103,7 +103,7 @@ public static void Example()
  model = ml.Model.Load(stream, out DataViewSchema schema);
 
  // We must create a new prediction engine from the persisted model.
- engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
+ engine = model.CreateTimeSeriesEngine<TimeSeriesData, ChangePointPrediction>(ml);
 
  // Run predictions on the loaded model.
  for (int i = 0; i < 5; i++)

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidChangePoint.cs

@@ -56,7 +56,7 @@ public static void Example()
  ITransformer model = ml.Transforms.DetectIidChangePoint(outputColumnName, inputColumnName, 95, Size / 4).Fit(dataView);
 
  // Create a time series prediction engine from the model.
- var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
+ var engine = model.CreateTimeSeriesEngine<TimeSeriesData, ChangePointPrediction>(ml);
 
  Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
  Console.WriteLine("Data\tAlert\tScore\tP-Value\tMartingale value");
@@ -97,7 +97,7 @@ public static void Example()
  model = ml.Model.Load(file, out DataViewSchema schema);
 
  // Create a time series prediction engine from the checkpointed model.
- engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
+ engine = model.CreateTimeSeriesEngine<TimeSeriesData, ChangePointPrediction>(ml);
  for (int index = 0; index < 8; index++)
  {
  // Anomaly change point detection.

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidSpike.cs

@@ -48,7 +48,7 @@ public static void Example()
  ITransformer model = ml.Transforms.DetectIidSpike(outputColumnName, inputColumnName, 95, Size).Fit(dataView);
 
  // Create a time series prediction engine from the model.
- var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, IidSpikePrediction>(ml);
+ var engine = model.CreateTimeSeriesEngine<TimeSeriesData, IidSpikePrediction>(ml);
 
  Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
  Console.WriteLine("Data\tAlert\tScore\tP-Value");

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectSpikeBySsa.cs

@@ -54,7 +54,7 @@ public static void Example()
  ITransformer model = ml.Transforms.DetectSpikeBySsa(outputColumnName, inputColumnName, 95, 8, TrainingSize, SeasonalitySize + 1).Fit(dataView);
 
  // Create a prediction engine from the model for feeding new data.
- var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, SsaSpikePrediction>(ml);
+ var engine = model.CreateTimeSeriesEngine<TimeSeriesData, SsaSpikePrediction>(ml);
 
  // Start streaming new data points with no change point to the prediction engine.
  Console.WriteLine($"Output from spike predictions on new data:");
@@ -94,7 +94,7 @@ public static void Example()
  model = ml.Model.Load(file, out DataViewSchema schema);
 
  // We must create a new prediction engine from the persisted model.
- engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, SsaSpikePrediction>(ml);
+ engine = model.CreateTimeSeriesEngine<TimeSeriesData, SsaSpikePrediction>(ml);
 
  // Run predictions on the loaded model.
  for (int i = 0; i < 5; i++)

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/Forecasting.cs

@@ -1,8 +1,8 @@
 using System;
 using System.Collections.Generic;
+using System.IO;
 using Microsoft.ML;
 using Microsoft.ML.Transforms.TimeSeries;
-using Microsoft.ML.TimeSeries;
 
 namespace Samples.Dynamic
 {
@@ -16,8 +16,7 @@ public static void Example()
  // as well as the source of randomness.
  var ml = new MLContext();
 
- // Generate sample series data with a recurring pattern
- const int SeasonalitySize = 5;
+ // Generate sample series data with a recurring pattern.
  var data = new List<TimeSeriesData>()
  {
  new TimeSeriesData(0),
@@ -44,43 +43,58 @@ public static void Example()
 
  // Setup arguments.
  var inputColumnName = nameof(TimeSeriesData.Value);
+ var outputColumnName = nameof(ForecastResult.Forecast);
 
  // Instantiate the forecasting model.
- var model = ml.Forecasting.AdaptiveSingularSpectrumSequenceModeler(inputColumnName, data.Count, SeasonalitySize + 1, SeasonalitySize,
- 1, AdaptiveSingularSpectrumSequenceModeler.RankSelectionMethod.Exact, null, SeasonalitySize / 2, false, false);
+ var model = ml.Forecasting.ForecastBySsa(outputColumnName, inputColumnName, 5, 11, data.Count, 5);
 
  // Train.
- model.Train(dataView);
+var transformer = model.Fit(dataView);
 
  // Forecast next five values.
- var forecast = model.Forecast(5);
+ var forecastEngine = transformer.CreateTimeSeriesEngine<TimeSeriesData, ForecastResult>(ml);
+ var forecast = forecastEngine.Predict();
+
  Console.WriteLine($"Forecasted values:");
- Console.WriteLine("[{0}]", string.Join(", ", forecast));
+ Console.WriteLine("[{0}]", string.Join(", ", forecast.Forecast));
  // Forecasted values:
- // [2.452744, 2.589339, 2.729183, 2.873005, 3.028931]
+ // [1.977226, 1.020494, 1.760543, 3.437509, 4.266461]
 
  // Update with new observations.
- dataView = ml.Data.LoadFromEnumerable(new List<TimeSeriesData>() { new TimeSeriesData(0), new TimeSeriesData(0), new TimeSeriesData(0), new TimeSeriesData(0) });
- model.Update(dataView);
+ forecastEngine.Predict(new TimeSeriesData(0));
+ forecastEngine.Predict(new TimeSeriesData(0));
+ forecastEngine.Predict(new TimeSeriesData(0));
+ forecastEngine.Predict(new TimeSeriesData(0));
 
  // Checkpoint.
-ml.Model.SaveForecastingModel(model, "model.zip");
+forecastEngine.CheckPoint(ml, "model.zip");
 
  // Load the checkpointed model from disk.
- var modelCopy = ml.Model.LoadForecastingModel<float>("model.zip");
+ // Load the model.
+ ITransformer modelCopy;
+ using (var file = File.OpenRead("model.zip"))
+ modelCopy = ml.Model.Load(file, out DataViewSchema schema);
+
+ // We must create a new prediction engine from the persisted model.
+ var forecastEngineCopy = modelCopy.CreateTimeSeriesEngine<TimeSeriesData, ForecastResult>(ml);
 
  // Forecast with the checkpointed model loaded from disk.
- forecast = modelCopy.Forecast(5);
- Console.WriteLine("[{0}]", string.Join(", ", forecast));
- // [0.8681176, 0.8185108, 0.8069275, 0.84405, 0.9455081]
+ forecast = forecastEngineCopy.Predict();
+ Console.WriteLine("[{0}]", string.Join(", ", forecast.Forecast));
+ // [1.791331, 1.255525, 0.3060154, -0.200446, 0.5657795]
 
  // Forecast with the original model(that was checkpointed to disk).
- forecast = model.Forecast(5);
- Console.WriteLine("[{0}]", string.Join(", ", forecast));
- // [0.8681176, 0.8185108, 0.8069275, 0.84405, 0.9455081]
+ forecast = forecastEngine.Predict();
+ Console.WriteLine("[{0}]", string.Join(", ", forecast.Forecast));
+ // [1.791331, 1.255525, 0.3060154, -0.200446, 0.5657795]
 
  }
 
+ class ForecastResult
+ {
+ public float[] Forecast { get; set; }
+ }
+
  class TimeSeriesData
  {
  public float Value;

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/ForecastingWithConfidenceInterval.cs

@@ -2,7 +2,7 @@
 using System.Collections.Generic;
 using Microsoft.ML;
 using Microsoft.ML.Transforms.TimeSeries;
-using Microsoft.ML.TimeSeries;
+using System.IO;
 
 namespace Samples.Dynamic
 {
@@ -16,8 +16,7 @@ public static void Example()
  // as well as the source of randomness.
  var ml = new MLContext();
 
- // Generate sample series data with a recurring pattern
- const int SeasonalitySize = 5;
+ // Generate sample series data with a recurring pattern.
  var data = new List<TimeSeriesData>()
  {
  new TimeSeriesData(0),
@@ -44,50 +43,58 @@ public static void Example()
 
  // Setup arguments.
  var inputColumnName = nameof(TimeSeriesData.Value);
+ var outputColumnName = nameof(ForecastResult.Forecast);
 
- // Instantiate forecasting model.
- var model = ml.Forecasting.AdaptiveSingularSpectrumSequenceModeler(inputColumnName, data.Count, SeasonalitySize + 1, SeasonalitySize,
- 1, AdaptiveSingularSpectrumSequenceModeler.RankSelectionMethod.Exact, null, SeasonalitySize / 2, shouldComputeForecastIntervals: true, false);
+ // Instantiate the forecasting model.
+ var model = ml.Forecasting.ForecastBySsa(outputColumnName, inputColumnName, 5, 11, data.Count, 5,
+ confidenceLevel: 0.95f,
+ forcastingConfidentLowerBoundColumnName: "ConfidenceLowerBound",
+ forcastingConfidentUpperBoundColumnName: "ConfidenceUpperBound");
 
  // Train.
- model.Train(dataView);
-
- // Forecast next five values with confidence internal.
- float[] forecast;
- float[] confidenceIntervalLowerBounds;
- float[] confidenceIntervalUpperBounds;
- model.ForecastWithConfidenceIntervals(5, out forecast, out confidenceIntervalLowerBounds, out confidenceIntervalUpperBounds);
- PrintForecastValuesAndIntervals(forecast, confidenceIntervalLowerBounds, confidenceIntervalUpperBounds);
+ var transformer = model.Fit(dataView);
+
+ // Forecast next five values.
+ var forecastEngine = transformer.CreateTimeSeriesEngine<TimeSeriesData, ForecastResult>(ml);
+ var forecast = forecastEngine.Predict();
+
+ PrintForecastValuesAndIntervals(forecast.Forecast, forecast.ConfidenceLowerBound, forecast.ConfidenceUpperBound);
  // Forecasted values:
- // [2.452744, 2.589339, 2.729183, 2.873005, 3.028931]
+ // [1.977226, 1.020494, 1.760543, 3.437509, 4.266461]
  // Confidence intervals:
- // [-0.2235315 - 5.12902] [-0.08777174 - 5.266451] [0.05076938 - 5.407597] [0.1925406 - 5.553469] [0.3469928 - 5.71087]
+ // [0.3451088 - 3.609343] [-0.7967533 - 2.83774] [-0.058467 - 3.579552] [1.61505 - 5.259968] [2.349299 - 6.183623]
 
  // Update with new observations.
- dataView = ml.Data.LoadFromEnumerable(new List<TimeSeriesData>() { new TimeSeriesData(0), new TimeSeriesData(0), new TimeSeriesData(0), new TimeSeriesData(0) });
- model.Update(dataView);
+ forecastEngine.Predict(new TimeSeriesData(0));
+ forecastEngine.Predict(new TimeSeriesData(0));
+ forecastEngine.Predict(new TimeSeriesData(0));
+ forecastEngine.Predict(new TimeSeriesData(0));
 
  // Checkpoint.
-ml.Model.SaveForecastingModel(model, "model.zip");
+forecastEngine.CheckPoint(ml, "model.zip");
 
  // Load the checkpointed model from disk.
- var modelCopy = ml.Model.LoadForecastingModel<float>("model.zip");
+ // Load the model.
+ ITransformer modelCopy;
+ using (var file = File.OpenRead("model.zip"))
+ modelCopy = ml.Model.Load(file, out DataViewSchema schema);
+
+ // We must create a new prediction engine from the persisted model.
+ var forecastEngineCopy = modelCopy.CreateTimeSeriesEngine<TimeSeriesData, ForecastResult>(ml);
 
  // Forecast with the checkpointed model loaded from disk.
- modelCopy.ForecastWithConfidenceIntervals(5, out forecast, out confidenceIntervalLowerBounds, out confidenceIntervalUpperBounds);
- PrintForecastValuesAndIntervals(forecast, confidenceIntervalLowerBounds, confidenceIntervalUpperBounds);
- // Forecasted values:
- // [0.8681176, 0.8185108, 0.8069275, 0.84405, 0.9455081]
+ forecast = forecastEngineCopy.Predict();
+ PrintForecastValuesAndIntervals(forecast.Forecast, forecast.ConfidenceLowerBound, forecast.ConfidenceUpperBound);
+ // [1.791331, 1.255525, 0.3060154, -0.200446, 0.5657795]
  // Confidence intervals:
- // [-1.808158 - 3.544394] [-1.8586 - 3.495622] [-1.871486 - 3.485341] [-1.836414 - 3.524514] [-1.736431 - 3.627447]
+ // [0.1592142 - 3.423448] [-0.5617217 - 3.072772] [-1.512994 - 2.125025] [-2.022905 - 1.622013] [-1.351382 - 2.482941]
 
  // Forecast with the original model(that was checkpointed to disk).
- model.ForecastWithConfidenceIntervals(5, out forecast, out confidenceIntervalLowerBounds, out confidenceIntervalUpperBounds);
- PrintForecastValuesAndIntervals(forecast, confidenceIntervalLowerBounds, confidenceIntervalUpperBounds);
- // Forecasted values:
- // [0.8681176, 0.8185108, 0.8069275, 0.84405, 0.9455081]
+ forecast = forecastEngine.Predict();
+ PrintForecastValuesAndIntervals(forecast.Forecast, forecast.ConfidenceLowerBound, forecast.ConfidenceUpperBound);
+ // [1.791331, 1.255525, 0.3060154, -0.200446, 0.5657795]
  // Confidence intervals:
- // [-1.808158 - 3.544394] [-1.8586 - 3.495622] [-1.871486 - 3.485341] [-1.836414 - 3.524514] [-1.736431 - 3.627447]
+ // [0.1592142 - 3.423448] [-0.5617217 - 3.072772] [-1.512994 - 2.125025] [-2.022905 - 1.622013] [-1.351382 - 2.482941]
  }
 
  static void PrintForecastValuesAndIntervals(float[] forecast, float[] confidenceIntervalLowerBounds, float[] confidenceIntervalUpperBounds)
@@ -100,6 +107,13 @@ static void PrintForecastValuesAndIntervals(float[] forecast, float[] confidence
  Console.WriteLine();
  }
 
+ class ForecastResult
+ {
+ public float[] Forecast { get; set; }
+ public float[] ConfidenceLowerBound { get; set; }
+ public float[] ConfidenceUpperBound { get; set; }
+ }
+
  class TimeSeriesData
  {
  public float Value;

docs/samples/Microsoft.ML.Samples/Program.cs

@@ -1,5 +1,6 @@
 using System;
 using System.Reflection;
+using Samples.Dynamic;
 
 namespace Microsoft.ML.Samples
 {

docs/api-reference/io-time-series-ssa-forecast.md

@@ -0,0 +1,5 @@
+### Input and Output Columns
+There is only one input column.
+The input column must be <xref:System.Single> where a <xref:System.Single> value indicates a value at a timestamp in the time series.
+
+It produces either just one vector of forecasted values or three vectors: a vector of forecasted values, a vector of confidence lower bounds and a vector of confidence upper bounds.

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectAnomalyBySrCnn.cs

@@ -40,7 +40,7 @@ public static void Example()
  ITransformer model = ml.Transforms.DetectAnomalyBySrCnn(outputColumnName, inputColumnName, 16, 5, 5, 3, 8, 0.35).Fit(dataView);
 
  // Create a time series prediction engine from the model.
- var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, SrCnnAnomalyDetection>(ml);
+ var engine = model.CreateTimeSeriesEngine<TimeSeriesData, SrCnnAnomalyDetection>(ml);
 
  Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
  Console.WriteLine("Data\tAlert\tScore\tMag");

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsa.cs

@@ -57,7 +57,7 @@ public static void Example()
  ITransformer model = ml.Transforms.DetectChangePointBySsa(outputColumnName, inputColumnName, confidence, changeHistoryLength, TrainingSize, SeasonalitySize + 1).Fit(dataView);
 
  // Create a prediction engine from the model for feeding new data.
- var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
+ var engine = model.CreateTimeSeriesEngine<TimeSeriesData, ChangePointPrediction>(ml);
 
  // Start streaming new data points with no change point to the prediction engine.
  Console.WriteLine($"Output from ChangePoint predictions on new data:");
@@ -99,7 +99,7 @@ public static void Example()
  model = ml.Model.Load(file, out DataViewSchema schema);
 
  // We must create a new prediction engine from the persisted model.
- engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
+ engine = model.CreateTimeSeriesEngine<TimeSeriesData, ChangePointPrediction>(ml);
 
  // Run predictions on the loaded model.
  for (int i = 0; i < 5; i++)

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsaStream.cs

@@ -57,7 +57,7 @@ public static void Example()
  ITransformer model = ml.Transforms.DetectChangePointBySsa(outputColumnName, inputColumnName, confidence, changeHistoryLength, TrainingSize, SeasonalitySize + 1).Fit(dataView);
 
  // Create a prediction engine from the model for feeding new data.
- var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
+ var engine = model.CreateTimeSeriesEngine<TimeSeriesData, ChangePointPrediction>(ml);
 
  // Start streaming new data points with no change point to the prediction engine.
  Console.WriteLine($"Output from ChangePoint predictions on new data:");
@@ -103,7 +103,7 @@ public static void Example()
  model = ml.Model.Load(stream, out DataViewSchema schema);
 
  // We must create a new prediction engine from the persisted model.
- engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
+ engine = model.CreateTimeSeriesEngine<TimeSeriesData, ChangePointPrediction>(ml);
 
  // Run predictions on the loaded model.
  for (int i = 0; i < 5; i++)

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidChangePoint.cs

@@ -56,7 +56,7 @@ public static void Example()
  ITransformer model = ml.Transforms.DetectIidChangePoint(outputColumnName, inputColumnName, 95, Size / 4).Fit(dataView);
 
  // Create a time series prediction engine from the model.
- var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
+ var engine = model.CreateTimeSeriesEngine<TimeSeriesData, ChangePointPrediction>(ml);
 
  Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
  Console.WriteLine("Data\tAlert\tScore\tP-Value\tMartingale value");
@@ -97,7 +97,7 @@ public static void Example()
  model = ml.Model.Load(file, out DataViewSchema schema);
 
  // Create a time series prediction engine from the checkpointed model.
- engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
+ engine = model.CreateTimeSeriesEngine<TimeSeriesData, ChangePointPrediction>(ml);
  for (int index = 0; index < 8; index++)
  {
  // Anomaly change point detection.

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidSpike.cs

@@ -48,7 +48,7 @@ public static void Example()
  ITransformer model = ml.Transforms.DetectIidSpike(outputColumnName, inputColumnName, 95, Size).Fit(dataView);
 
  // Create a time series prediction engine from the model.
- var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, IidSpikePrediction>(ml);
+ var engine = model.CreateTimeSeriesEngine<TimeSeriesData, IidSpikePrediction>(ml);
 
  Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
  Console.WriteLine("Data\tAlert\tScore\tP-Value");

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectSpikeBySsa.cs

@@ -54,7 +54,7 @@ public static void Example()
  ITransformer model = ml.Transforms.DetectSpikeBySsa(outputColumnName, inputColumnName, 95, 8, TrainingSize, SeasonalitySize + 1).Fit(dataView);
 
  // Create a prediction engine from the model for feeding new data.
- var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, SsaSpikePrediction>(ml);
+ var engine = model.CreateTimeSeriesEngine<TimeSeriesData, SsaSpikePrediction>(ml);
 
  // Start streaming new data points with no change point to the prediction engine.
  Console.WriteLine($"Output from spike predictions on new data:");
@@ -94,7 +94,7 @@ public static void Example()
  model = ml.Model.Load(file, out DataViewSchema schema);
 
  // We must create a new prediction engine from the persisted model.
- engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, SsaSpikePrediction>(ml);
+ engine = model.CreateTimeSeriesEngine<TimeSeriesData, SsaSpikePrediction>(ml);
 
  // Run predictions on the loaded model.
  for (int i = 0; i < 5; i++)

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/Forecasting.cs

@@ -1,8 +1,8 @@
 using System;
 using System.Collections.Generic;
+using System.IO;
 using Microsoft.ML;
 using Microsoft.ML.Transforms.TimeSeries;
-using Microsoft.ML.TimeSeries;
 
 namespace Samples.Dynamic
 {
@@ -16,8 +16,7 @@ public static void Example()
  // as well as the source of randomness.
  var ml = new MLContext();
 
- // Generate sample series data with a recurring pattern
- const int SeasonalitySize = 5;
+ // Generate sample series data with a recurring pattern.
  var data = new List<TimeSeriesData>()
  {
  new TimeSeriesData(0),
@@ -44,43 +43,58 @@ public static void Example()
 
  // Setup arguments.
  var inputColumnName = nameof(TimeSeriesData.Value);
+ var outputColumnName = nameof(ForecastResult.Forecast);
 
  // Instantiate the forecasting model.
- var model = ml.Forecasting.AdaptiveSingularSpectrumSequenceModeler(inputColumnName, data.Count, SeasonalitySize + 1, SeasonalitySize,
- 1, AdaptiveSingularSpectrumSequenceModeler.RankSelectionMethod.Exact, null, SeasonalitySize / 2, false, false);
+ var model = ml.Forecasting.ForecastBySsa(outputColumnName, inputColumnName, 5, 11, data.Count, 5);
 
  // Train.
- model.Train(dataView);
+var transformer = model.Fit(dataView);
 
  // Forecast next five values.
- var forecast = model.Forecast(5);
+ var forecastEngine = transformer.CreateTimeSeriesEngine<TimeSeriesData, ForecastResult>(ml);
+ var forecast = forecastEngine.Predict();
+
  Console.WriteLine($"Forecasted values:");
- Console.WriteLine("[{0}]", string.Join(", ", forecast));
+ Console.WriteLine("[{0}]", string.Join(", ", forecast.Forecast));
  // Forecasted values:
- // [2.452744, 2.589339, 2.729183, 2.873005, 3.028931]
+ // [1.977226, 1.020494, 1.760543, 3.437509, 4.266461]
 
  // Update with new observations.
- dataView = ml.Data.LoadFromEnumerable(new List<TimeSeriesData>() { new TimeSeriesData(0), new TimeSeriesData(0), new TimeSeriesData(0), new TimeSeriesData(0) });
- model.Update(dataView);
+ forecastEngine.Predict(new TimeSeriesData(0));
+ forecastEngine.Predict(new TimeSeriesData(0));
+ forecastEngine.Predict(new TimeSeriesData(0));
+ forecastEngine.Predict(new TimeSeriesData(0));
 
  // Checkpoint.
-ml.Model.SaveForecastingModel(model, "model.zip");
+forecastEngine.CheckPoint(ml, "model.zip");
 
  // Load the checkpointed model from disk.
- var modelCopy = ml.Model.LoadForecastingModel<float>("model.zip");
+ // Load the model.
+ ITransformer modelCopy;
+ using (var file = File.OpenRead("model.zip"))
+ modelCopy = ml.Model.Load(file, out DataViewSchema schema);
+
+ // We must create a new prediction engine from the persisted model.
+ var forecastEngineCopy = modelCopy.CreateTimeSeriesEngine<TimeSeriesData, ForecastResult>(ml);
 
  // Forecast with the checkpointed model loaded from disk.
- forecast = modelCopy.Forecast(5);
- Console.WriteLine("[{0}]", string.Join(", ", forecast));
- // [0.8681176, 0.8185108, 0.8069275, 0.84405, 0.9455081]
+ forecast = forecastEngineCopy.Predict();
+ Console.WriteLine("[{0}]", string.Join(", ", forecast.Forecast));
+ // [1.791331, 1.255525, 0.3060154, -0.200446, 0.5657795]
 
  // Forecast with the original model(that was checkpointed to disk).
- forecast = model.Forecast(5);
- Console.WriteLine("[{0}]", string.Join(", ", forecast));
- // [0.8681176, 0.8185108, 0.8069275, 0.84405, 0.9455081]
+ forecast = forecastEngine.Predict();
+ Console.WriteLine("[{0}]", string.Join(", ", forecast.Forecast));
+ // [1.791331, 1.255525, 0.3060154, -0.200446, 0.5657795]
 
  }
 
+ class ForecastResult
+ {
+ public float[] Forecast { get; set; }
+ }
+
  class TimeSeriesData
  {
  public float Value;

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/ForecastingWithConfidenceInterval.cs

@@ -2,7 +2,7 @@
 using System.Collections.Generic;
 using Microsoft.ML;
 using Microsoft.ML.Transforms.TimeSeries;
-using Microsoft.ML.TimeSeries;
+using System.IO;
 
 namespace Samples.Dynamic
 {
@@ -16,8 +16,7 @@ public static void Example()
  // as well as the source of randomness.
  var ml = new MLContext();
 
- // Generate sample series data with a recurring pattern
- const int SeasonalitySize = 5;
+ // Generate sample series data with a recurring pattern.
  var data = new List<TimeSeriesData>()
  {
  new TimeSeriesData(0),
@@ -44,50 +43,58 @@ public static void Example()
 
  // Setup arguments.
  var inputColumnName = nameof(TimeSeriesData.Value);
+ var outputColumnName = nameof(ForecastResult.Forecast);
 
- // Instantiate forecasting model.
- var model = ml.Forecasting.AdaptiveSingularSpectrumSequenceModeler(inputColumnName, data.Count, SeasonalitySize + 1, SeasonalitySize,
- 1, AdaptiveSingularSpectrumSequenceModeler.RankSelectionMethod.Exact, null, SeasonalitySize / 2, shouldComputeForecastIntervals: true, false);
+ // Instantiate the forecasting model.
+ var model = ml.Forecasting.ForecastBySsa(outputColumnName, inputColumnName, 5, 11, data.Count, 5,
+ confidenceLevel: 0.95f,
+ forcastingConfidentLowerBoundColumnName: "ConfidenceLowerBound",
+ forcastingConfidentUpperBoundColumnName: "ConfidenceUpperBound");
 
  // Train.
- model.Train(dataView);
-
- // Forecast next five values with confidence internal.
- float[] forecast;
- float[] confidenceIntervalLowerBounds;
- float[] confidenceIntervalUpperBounds;
- model.ForecastWithConfidenceIntervals(5, out forecast, out confidenceIntervalLowerBounds, out confidenceIntervalUpperBounds);
- PrintForecastValuesAndIntervals(forecast, confidenceIntervalLowerBounds, confidenceIntervalUpperBounds);
+ var transformer = model.Fit(dataView);
+
+ // Forecast next five values.
+ var forecastEngine = transformer.CreateTimeSeriesEngine<TimeSeriesData, ForecastResult>(ml);
+ var forecast = forecastEngine.Predict();
+
+ PrintForecastValuesAndIntervals(forecast.Forecast, forecast.ConfidenceLowerBound, forecast.ConfidenceUpperBound);
  // Forecasted values:
- // [2.452744, 2.589339, 2.729183, 2.873005, 3.028931]
+ // [1.977226, 1.020494, 1.760543, 3.437509, 4.266461]
  // Confidence intervals:
- // [-0.2235315 - 5.12902] [-0.08777174 - 5.266451] [0.05076938 - 5.407597] [0.1925406 - 5.553469] [0.3469928 - 5.71087]
+ // [0.3451088 - 3.609343] [-0.7967533 - 2.83774] [-0.058467 - 3.579552] [1.61505 - 5.259968] [2.349299 - 6.183623]
 
  // Update with new observations.
- dataView = ml.Data.LoadFromEnumerable(new List<TimeSeriesData>() { new TimeSeriesData(0), new TimeSeriesData(0), new TimeSeriesData(0), new TimeSeriesData(0) });
- model.Update(dataView);
+ forecastEngine.Predict(new TimeSeriesData(0));
+ forecastEngine.Predict(new TimeSeriesData(0));
+ forecastEngine.Predict(new TimeSeriesData(0));
+ forecastEngine.Predict(new TimeSeriesData(0));
 
  // Checkpoint.
-ml.Model.SaveForecastingModel(model, "model.zip");
+forecastEngine.CheckPoint(ml, "model.zip");
 
  // Load the checkpointed model from disk.
- var modelCopy = ml.Model.LoadForecastingModel<float>("model.zip");
+ // Load the model.
+ ITransformer modelCopy;
+ using (var file = File.OpenRead("model.zip"))
+ modelCopy = ml.Model.Load(file, out DataViewSchema schema);
+
+ // We must create a new prediction engine from the persisted model.
+ var forecastEngineCopy = modelCopy.CreateTimeSeriesEngine<TimeSeriesData, ForecastResult>(ml);
 
  // Forecast with the checkpointed model loaded from disk.
- modelCopy.ForecastWithConfidenceIntervals(5, out forecast, out confidenceIntervalLowerBounds, out confidenceIntervalUpperBounds);
- PrintForecastValuesAndIntervals(forecast, confidenceIntervalLowerBounds, confidenceIntervalUpperBounds);
- // Forecasted values:
- // [0.8681176, 0.8185108, 0.8069275, 0.84405, 0.9455081]
+ forecast = forecastEngineCopy.Predict();
+ PrintForecastValuesAndIntervals(forecast.Forecast, forecast.ConfidenceLowerBound, forecast.ConfidenceUpperBound);
+ // [1.791331, 1.255525, 0.3060154, -0.200446, 0.5657795]
  // Confidence intervals:
- // [-1.808158 - 3.544394] [-1.8586 - 3.495622] [-1.871486 - 3.485341] [-1.836414 - 3.524514] [-1.736431 - 3.627447]
+ // [0.1592142 - 3.423448] [-0.5617217 - 3.072772] [-1.512994 - 2.125025] [-2.022905 - 1.622013] [-1.351382 - 2.482941]
 
  // Forecast with the original model(that was checkpointed to disk).
- model.ForecastWithConfidenceIntervals(5, out forecast, out confidenceIntervalLowerBounds, out confidenceIntervalUpperBounds);
- PrintForecastValuesAndIntervals(forecast, confidenceIntervalLowerBounds, confidenceIntervalUpperBounds);
- // Forecasted values:
- // [0.8681176, 0.8185108, 0.8069275, 0.84405, 0.9455081]
+ forecast = forecastEngine.Predict();
+ PrintForecastValuesAndIntervals(forecast.Forecast, forecast.ConfidenceLowerBound, forecast.ConfidenceUpperBound);
+ // [1.791331, 1.255525, 0.3060154, -0.200446, 0.5657795]
  // Confidence intervals:
- // [-1.808158 - 3.544394] [-1.8586 - 3.495622] [-1.871486 - 3.485341] [-1.836414 - 3.524514] [-1.736431 - 3.627447]
+ // [0.1592142 - 3.423448] [-0.5617217 - 3.072772] [-1.512994 - 2.125025] [-2.022905 - 1.622013] [-1.351382 - 2.482941]
  }
 
  static void PrintForecastValuesAndIntervals(float[] forecast, float[] confidenceIntervalLowerBounds, float[] confidenceIntervalUpperBounds)
@@ -100,6 +107,13 @@ static void PrintForecastValuesAndIntervals(float[] forecast, float[] confidence
  Console.WriteLine();
  }
 
+ class ForecastResult
+ {
+ public float[] Forecast { get; set; }
+ public float[] ConfidenceLowerBound { get; set; }
+ public float[] ConfidenceUpperBound { get; set; }
+ }
+
  class TimeSeriesData
  {
  public float Value;

docs/samples/Microsoft.ML.Samples/Program.cs

@@ -1,5 +1,6 @@
 using System;
 using System.Reflection;
+using Samples.Dynamic;
 
 namespace Microsoft.ML.Samples
 {

docs/api-reference/io-time-series-ssa-forecast.md

@@ -0,0 +1,5 @@
+### Input and Output Columns
+There is only one input column.
+The input column must be <xref:System.Single> where a <xref:System.Single> value indicates a value at a timestamp in the time series.
+
+It produces either just one vector of forecasted values or three vectors: a vector of forecasted values, a vector of confidence lower bounds and a vector of confidence upper bounds.

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectAnomalyBySrCnn.cs

@@ -40,7 +40,7 @@ public static void Example()
  ITransformer model = ml.Transforms.DetectAnomalyBySrCnn(outputColumnName, inputColumnName, 16, 5, 5, 3, 8, 0.35).Fit(dataView);
 
  // Create a time series prediction engine from the model.
- var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, SrCnnAnomalyDetection>(ml);
+ var engine = model.CreateTimeSeriesEngine<TimeSeriesData, SrCnnAnomalyDetection>(ml);
 
  Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
  Console.WriteLine("Data\tAlert\tScore\tMag");

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsa.cs

@@ -57,7 +57,7 @@ public static void Example()
  ITransformer model = ml.Transforms.DetectChangePointBySsa(outputColumnName, inputColumnName, confidence, changeHistoryLength, TrainingSize, SeasonalitySize + 1).Fit(dataView);
 
  // Create a prediction engine from the model for feeding new data.
- var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
+ var engine = model.CreateTimeSeriesEngine<TimeSeriesData, ChangePointPrediction>(ml);
 
  // Start streaming new data points with no change point to the prediction engine.
  Console.WriteLine($"Output from ChangePoint predictions on new data:");
@@ -99,7 +99,7 @@ public static void Example()
  model = ml.Model.Load(file, out DataViewSchema schema);
 
  // We must create a new prediction engine from the persisted model.
- engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
+ engine = model.CreateTimeSeriesEngine<TimeSeriesData, ChangePointPrediction>(ml);
 
  // Run predictions on the loaded model.
  for (int i = 0; i < 5; i++)

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsaStream.cs

@@ -57,7 +57,7 @@ public static void Example()
  ITransformer model = ml.Transforms.DetectChangePointBySsa(outputColumnName, inputColumnName, confidence, changeHistoryLength, TrainingSize, SeasonalitySize + 1).Fit(dataView);
 
  // Create a prediction engine from the model for feeding new data.
- var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
+ var engine = model.CreateTimeSeriesEngine<TimeSeriesData, ChangePointPrediction>(ml);
 
  // Start streaming new data points with no change point to the prediction engine.
  Console.WriteLine($"Output from ChangePoint predictions on new data:");
@@ -103,7 +103,7 @@ public static void Example()
  model = ml.Model.Load(stream, out DataViewSchema schema);
 
  // We must create a new prediction engine from the persisted model.
- engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
+ engine = model.CreateTimeSeriesEngine<TimeSeriesData, ChangePointPrediction>(ml);
 
  // Run predictions on the loaded model.
  for (int i = 0; i < 5; i++)

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidChangePoint.cs

@@ -56,7 +56,7 @@ public static void Example()
  ITransformer model = ml.Transforms.DetectIidChangePoint(outputColumnName, inputColumnName, 95, Size / 4).Fit(dataView);
 
  // Create a time series prediction engine from the model.
- var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
+ var engine = model.CreateTimeSeriesEngine<TimeSeriesData, ChangePointPrediction>(ml);
 
  Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
  Console.WriteLine("Data\tAlert\tScore\tP-Value\tMartingale value");
@@ -97,7 +97,7 @@ public static void Example()
  model = ml.Model.Load(file, out DataViewSchema schema);
 
  // Create a time series prediction engine from the checkpointed model.
- engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
+ engine = model.CreateTimeSeriesEngine<TimeSeriesData, ChangePointPrediction>(ml);
  for (int index = 0; index < 8; index++)
  {
  // Anomaly change point detection.

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidSpike.cs

@@ -48,7 +48,7 @@ public static void Example()
  ITransformer model = ml.Transforms.DetectIidSpike(outputColumnName, inputColumnName, 95, Size).Fit(dataView);
 
  // Create a time series prediction engine from the model.
- var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, IidSpikePrediction>(ml);
+ var engine = model.CreateTimeSeriesEngine<TimeSeriesData, IidSpikePrediction>(ml);
 
  Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
  Console.WriteLine("Data\tAlert\tScore\tP-Value");

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectSpikeBySsa.cs

@@ -54,7 +54,7 @@ public static void Example()
  ITransformer model = ml.Transforms.DetectSpikeBySsa(outputColumnName, inputColumnName, 95, 8, TrainingSize, SeasonalitySize + 1).Fit(dataView);
 
  // Create a prediction engine from the model for feeding new data.
- var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, SsaSpikePrediction>(ml);
+ var engine = model.CreateTimeSeriesEngine<TimeSeriesData, SsaSpikePrediction>(ml);
 
  // Start streaming new data points with no change point to the prediction engine.
  Console.WriteLine($"Output from spike predictions on new data:");
@@ -94,7 +94,7 @@ public static void Example()
  model = ml.Model.Load(file, out DataViewSchema schema);
 
  // We must create a new prediction engine from the persisted model.
- engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, SsaSpikePrediction>(ml);
+ engine = model.CreateTimeSeriesEngine<TimeSeriesData, SsaSpikePrediction>(ml);
 
  // Run predictions on the loaded model.
  for (int i = 0; i < 5; i++)

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/Forecasting.cs

@@ -1,8 +1,8 @@
 using System;
 using System.Collections.Generic;
+using System.IO;
 using Microsoft.ML;
 using Microsoft.ML.Transforms.TimeSeries;
-using Microsoft.ML.TimeSeries;
 
 namespace Samples.Dynamic
 {
@@ -16,8 +16,7 @@ public static void Example()
  // as well as the source of randomness.
  var ml = new MLContext();
 
- // Generate sample series data with a recurring pattern
- const int SeasonalitySize = 5;
+ // Generate sample series data with a recurring pattern.
  var data = new List<TimeSeriesData>()
  {
  new TimeSeriesData(0),
@@ -44,43 +43,58 @@ public static void Example()
 
  // Setup arguments.
  var inputColumnName = nameof(TimeSeriesData.Value);
+ var outputColumnName = nameof(ForecastResult.Forecast);
 
  // Instantiate the forecasting model.
- var model = ml.Forecasting.AdaptiveSingularSpectrumSequenceModeler(inputColumnName, data.Count, SeasonalitySize + 1, SeasonalitySize,
- 1, AdaptiveSingularSpectrumSequenceModeler.RankSelectionMethod.Exact, null, SeasonalitySize / 2, false, false);
+ var model = ml.Forecasting.ForecastBySsa(outputColumnName, inputColumnName, 5, 11, data.Count, 5);
 
  // Train.
- model.Train(dataView);
+var transformer = model.Fit(dataView);
 
  // Forecast next five values.
- var forecast = model.Forecast(5);
+ var forecastEngine = transformer.CreateTimeSeriesEngine<TimeSeriesData, ForecastResult>(ml);
+ var forecast = forecastEngine.Predict();
+
  Console.WriteLine($"Forecasted values:");
- Console.WriteLine("[{0}]", string.Join(", ", forecast));
+ Console.WriteLine("[{0}]", string.Join(", ", forecast.Forecast));
  // Forecasted values:
- // [2.452744, 2.589339, 2.729183, 2.873005, 3.028931]
+ // [1.977226, 1.020494, 1.760543, 3.437509, 4.266461]
 
  // Update with new observations.
- dataView = ml.Data.LoadFromEnumerable(new List<TimeSeriesData>() { new TimeSeriesData(0), new TimeSeriesData(0), new TimeSeriesData(0), new TimeSeriesData(0) });
- model.Update(dataView);
+ forecastEngine.Predict(new TimeSeriesData(0));
+ forecastEngine.Predict(new TimeSeriesData(0));
+ forecastEngine.Predict(new TimeSeriesData(0));
+ forecastEngine.Predict(new TimeSeriesData(0));
 
  // Checkpoint.
-ml.Model.SaveForecastingModel(model, "model.zip");
+forecastEngine.CheckPoint(ml, "model.zip");
 
  // Load the checkpointed model from disk.
- var modelCopy = ml.Model.LoadForecastingModel<float>("model.zip");
+ // Load the model.
+ ITransformer modelCopy;
+ using (var file = File.OpenRead("model.zip"))
+ modelCopy = ml.Model.Load(file, out DataViewSchema schema);
+
+ // We must create a new prediction engine from the persisted model.
+ var forecastEngineCopy = modelCopy.CreateTimeSeriesEngine<TimeSeriesData, ForecastResult>(ml);
 
  // Forecast with the checkpointed model loaded from disk.
- forecast = modelCopy.Forecast(5);
- Console.WriteLine("[{0}]", string.Join(", ", forecast));
- // [0.8681176, 0.8185108, 0.8069275, 0.84405, 0.9455081]
+ forecast = forecastEngineCopy.Predict();
+ Console.WriteLine("[{0}]", string.Join(", ", forecast.Forecast));
+ // [1.791331, 1.255525, 0.3060154, -0.200446, 0.5657795]
 
  // Forecast with the original model(that was checkpointed to disk).
- forecast = model.Forecast(5);
- Console.WriteLine("[{0}]", string.Join(", ", forecast));
- // [0.8681176, 0.8185108, 0.8069275, 0.84405, 0.9455081]
+ forecast = forecastEngine.Predict();
+ Console.WriteLine("[{0}]", string.Join(", ", forecast.Forecast));
+ // [1.791331, 1.255525, 0.3060154, -0.200446, 0.5657795]
 
  }
 
+ class ForecastResult
+ {
+ public float[] Forecast { get; set; }
+ }
+
  class TimeSeriesData
  {
  public float Value;

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/ForecastingWithConfidenceInterval.cs

@@ -2,7 +2,7 @@
 using System.Collections.Generic;
 using Microsoft.ML;
 using Microsoft.ML.Transforms.TimeSeries;
-using Microsoft.ML.TimeSeries;
+using System.IO;
 
 namespace Samples.Dynamic
 {
@@ -16,8 +16,7 @@ public static void Example()
  // as well as the source of randomness.
  var ml = new MLContext();
 
- // Generate sample series data with a recurring pattern
- const int SeasonalitySize = 5;
+ // Generate sample series data with a recurring pattern.
  var data = new List<TimeSeriesData>()
  {
  new TimeSeriesData(0),
@@ -44,50 +43,58 @@ public static void Example()
 
  // Setup arguments.
  var inputColumnName = nameof(TimeSeriesData.Value);
+ var outputColumnName = nameof(ForecastResult.Forecast);
 
- // Instantiate forecasting model.
- var model = ml.Forecasting.AdaptiveSingularSpectrumSequenceModeler(inputColumnName, data.Count, SeasonalitySize + 1, SeasonalitySize,
- 1, AdaptiveSingularSpectrumSequenceModeler.RankSelectionMethod.Exact, null, SeasonalitySize / 2, shouldComputeForecastIntervals: true, false);
+ // Instantiate the forecasting model.
+ var model = ml.Forecasting.ForecastBySsa(outputColumnName, inputColumnName, 5, 11, data.Count, 5,
+ confidenceLevel: 0.95f,
+ forcastingConfidentLowerBoundColumnName: "ConfidenceLowerBound",
+ forcastingConfidentUpperBoundColumnName: "ConfidenceUpperBound");
 
  // Train.
- model.Train(dataView);
-
- // Forecast next five values with confidence internal.
- float[] forecast;
- float[] confidenceIntervalLowerBounds;
- float[] confidenceIntervalUpperBounds;
- model.ForecastWithConfidenceIntervals(5, out forecast, out confidenceIntervalLowerBounds, out confidenceIntervalUpperBounds);
- PrintForecastValuesAndIntervals(forecast, confidenceIntervalLowerBounds, confidenceIntervalUpperBounds);
+ var transformer = model.Fit(dataView);
+
+ // Forecast next five values.
+ var forecastEngine = transformer.CreateTimeSeriesEngine<TimeSeriesData, ForecastResult>(ml);
+ var forecast = forecastEngine.Predict();
+
+ PrintForecastValuesAndIntervals(forecast.Forecast, forecast.ConfidenceLowerBound, forecast.ConfidenceUpperBound);
  // Forecasted values:
- // [2.452744, 2.589339, 2.729183, 2.873005, 3.028931]
+ // [1.977226, 1.020494, 1.760543, 3.437509, 4.266461]
  // Confidence intervals:
- // [-0.2235315 - 5.12902] [-0.08777174 - 5.266451] [0.05076938 - 5.407597] [0.1925406 - 5.553469] [0.3469928 - 5.71087]
+ // [0.3451088 - 3.609343] [-0.7967533 - 2.83774] [-0.058467 - 3.579552] [1.61505 - 5.259968] [2.349299 - 6.183623]
 
  // Update with new observations.
- dataView = ml.Data.LoadFromEnumerable(new List<TimeSeriesData>() { new TimeSeriesData(0), new TimeSeriesData(0), new TimeSeriesData(0), new TimeSeriesData(0) });
- model.Update(dataView);
+ forecastEngine.Predict(new TimeSeriesData(0));
+ forecastEngine.Predict(new TimeSeriesData(0));
+ forecastEngine.Predict(new TimeSeriesData(0));
+ forecastEngine.Predict(new TimeSeriesData(0));
 
  // Checkpoint.
-ml.Model.SaveForecastingModel(model, "model.zip");
+forecastEngine.CheckPoint(ml, "model.zip");
 
  // Load the checkpointed model from disk.
- var modelCopy = ml.Model.LoadForecastingModel<float>("model.zip");
+ // Load the model.
+ ITransformer modelCopy;
+ using (var file = File.OpenRead("model.zip"))
+ modelCopy = ml.Model.Load(file, out DataViewSchema schema);
+
+ // We must create a new prediction engine from the persisted model.
+ var forecastEngineCopy = modelCopy.CreateTimeSeriesEngine<TimeSeriesData, ForecastResult>(ml);
 
  // Forecast with the checkpointed model loaded from disk.
- modelCopy.ForecastWithConfidenceIntervals(5, out forecast, out confidenceIntervalLowerBounds, out confidenceIntervalUpperBounds);
- PrintForecastValuesAndIntervals(forecast, confidenceIntervalLowerBounds, confidenceIntervalUpperBounds);
- // Forecasted values:
- // [0.8681176, 0.8185108, 0.8069275, 0.84405, 0.9455081]
+ forecast = forecastEngineCopy.Predict();
+ PrintForecastValuesAndIntervals(forecast.Forecast, forecast.ConfidenceLowerBound, forecast.ConfidenceUpperBound);
+ // [1.791331, 1.255525, 0.3060154, -0.200446, 0.5657795]
  // Confidence intervals:
- // [-1.808158 - 3.544394] [-1.8586 - 3.495622] [-1.871486 - 3.485341] [-1.836414 - 3.524514] [-1.736431 - 3.627447]
+ // [0.1592142 - 3.423448] [-0.5617217 - 3.072772] [-1.512994 - 2.125025] [-2.022905 - 1.622013] [-1.351382 - 2.482941]
 
  // Forecast with the original model(that was checkpointed to disk).
- model.ForecastWithConfidenceIntervals(5, out forecast, out confidenceIntervalLowerBounds, out confidenceIntervalUpperBounds);
- PrintForecastValuesAndIntervals(forecast, confidenceIntervalLowerBounds, confidenceIntervalUpperBounds);
- // Forecasted values:
- // [0.8681176, 0.8185108, 0.8069275, 0.84405, 0.9455081]
+ forecast = forecastEngine.Predict();
+ PrintForecastValuesAndIntervals(forecast.Forecast, forecast.ConfidenceLowerBound, forecast.ConfidenceUpperBound);
+ // [1.791331, 1.255525, 0.3060154, -0.200446, 0.5657795]
  // Confidence intervals:
- // [-1.808158 - 3.544394] [-1.8586 - 3.495622] [-1.871486 - 3.485341] [-1.836414 - 3.524514] [-1.736431 - 3.627447]
+ // [0.1592142 - 3.423448] [-0.5617217 - 3.072772] [-1.512994 - 2.125025] [-2.022905 - 1.622013] [-1.351382 - 2.482941]
  }
 
  static void PrintForecastValuesAndIntervals(float[] forecast, float[] confidenceIntervalLowerBounds, float[] confidenceIntervalUpperBounds)
@@ -100,6 +107,13 @@ static void PrintForecastValuesAndIntervals(float[] forecast, float[] confidence
  Console.WriteLine();
  }
 
+ class ForecastResult
+ {
+ public float[] Forecast { get; set; }
+ public float[] ConfidenceLowerBound { get; set; }
+ public float[] ConfidenceUpperBound { get; set; }
+ }
+
  class TimeSeriesData
  {
  public float Value;

docs/samples/Microsoft.ML.Samples/Program.cs

@@ -1,5 +1,6 @@
 using System;
 using System.Reflection;
+using Samples.Dynamic;
 
 namespace Microsoft.ML.Samples
 {

docs/api-reference/io-time-series-ssa-forecast.md

@@ -0,0 +1,5 @@
+### Input and Output Columns
+There is only one input column.
+The input column must be <xref:System.Single> where a <xref:System.Single> value indicates a value at a timestamp in the time series.
+
+It produces either just one vector of forecasted values or three vectors: a vector of forecasted values, a vector of confidence lower bounds and a vector of confidence upper bounds.

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectAnomalyBySrCnn.cs

@@ -40,7 +40,7 @@ public static void Example()
  ITransformer model = ml.Transforms.DetectAnomalyBySrCnn(outputColumnName, inputColumnName, 16, 5, 5, 3, 8, 0.35).Fit(dataView);
 
  // Create a time series prediction engine from the model.
- var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, SrCnnAnomalyDetection>(ml);
+ var engine = model.CreateTimeSeriesEngine<TimeSeriesData, SrCnnAnomalyDetection>(ml);
 
  Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
  Console.WriteLine("Data\tAlert\tScore\tMag");

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsa.cs

@@ -57,7 +57,7 @@ public static void Example()
  ITransformer model = ml.Transforms.DetectChangePointBySsa(outputColumnName, inputColumnName, confidence, changeHistoryLength, TrainingSize, SeasonalitySize + 1).Fit(dataView);
 
  // Create a prediction engine from the model for feeding new data.
- var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
+ var engine = model.CreateTimeSeriesEngine<TimeSeriesData, ChangePointPrediction>(ml);
 
  // Start streaming new data points with no change point to the prediction engine.
  Console.WriteLine($"Output from ChangePoint predictions on new data:");
@@ -99,7 +99,7 @@ public static void Example()
  model = ml.Model.Load(file, out DataViewSchema schema);
 
  // We must create a new prediction engine from the persisted model.
- engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
+ engine = model.CreateTimeSeriesEngine<TimeSeriesData, ChangePointPrediction>(ml);
 
  // Run predictions on the loaded model.
  for (int i = 0; i < 5; i++)

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsaStream.cs

@@ -57,7 +57,7 @@ public static void Example()
  ITransformer model = ml.Transforms.DetectChangePointBySsa(outputColumnName, inputColumnName, confidence, changeHistoryLength, TrainingSize, SeasonalitySize + 1).Fit(dataView);
 
  // Create a prediction engine from the model for feeding new data.
- var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
+ var engine = model.CreateTimeSeriesEngine<TimeSeriesData, ChangePointPrediction>(ml);
 
  // Start streaming new data points with no change point to the prediction engine.
  Console.WriteLine($"Output from ChangePoint predictions on new data:");
@@ -103,7 +103,7 @@ public static void Example()
  model = ml.Model.Load(stream, out DataViewSchema schema);
 
  // We must create a new prediction engine from the persisted model.
- engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
+ engine = model.CreateTimeSeriesEngine<TimeSeriesData, ChangePointPrediction>(ml);
 
  // Run predictions on the loaded model.
  for (int i = 0; i < 5; i++)

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidChangePoint.cs

@@ -56,7 +56,7 @@ public static void Example()
  ITransformer model = ml.Transforms.DetectIidChangePoint(outputColumnName, inputColumnName, 95, Size / 4).Fit(dataView);
 
  // Create a time series prediction engine from the model.
- var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
+ var engine = model.CreateTimeSeriesEngine<TimeSeriesData, ChangePointPrediction>(ml);
 
  Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
  Console.WriteLine("Data\tAlert\tScore\tP-Value\tMartingale value");
@@ -97,7 +97,7 @@ public static void Example()
  model = ml.Model.Load(file, out DataViewSchema schema);
 
  // Create a time series prediction engine from the checkpointed model.
- engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
+ engine = model.CreateTimeSeriesEngine<TimeSeriesData, ChangePointPrediction>(ml);
  for (int index = 0; index < 8; index++)
  {
  // Anomaly change point detection.

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidSpike.cs

@@ -48,7 +48,7 @@ public static void Example()
  ITransformer model = ml.Transforms.DetectIidSpike(outputColumnName, inputColumnName, 95, Size).Fit(dataView);
 
  // Create a time series prediction engine from the model.
- var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, IidSpikePrediction>(ml);
+ var engine = model.CreateTimeSeriesEngine<TimeSeriesData, IidSpikePrediction>(ml);
 
  Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
  Console.WriteLine("Data\tAlert\tScore\tP-Value");

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectSpikeBySsa.cs

@@ -54,7 +54,7 @@ public static void Example()
  ITransformer model = ml.Transforms.DetectSpikeBySsa(outputColumnName, inputColumnName, 95, 8, TrainingSize, SeasonalitySize + 1).Fit(dataView);
 
  // Create a prediction engine from the model for feeding new data.
- var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, SsaSpikePrediction>(ml);
+ var engine = model.CreateTimeSeriesEngine<TimeSeriesData, SsaSpikePrediction>(ml);
 
  // Start streaming new data points with no change point to the prediction engine.
  Console.WriteLine($"Output from spike predictions on new data:");
@@ -94,7 +94,7 @@ public static void Example()
  model = ml.Model.Load(file, out DataViewSchema schema);
 
  // We must create a new prediction engine from the persisted model.
- engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, SsaSpikePrediction>(ml);
+ engine = model.CreateTimeSeriesEngine<TimeSeriesData, SsaSpikePrediction>(ml);
 
  // Run predictions on the loaded model.
  for (int i = 0; i < 5; i++)

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/Forecasting.cs

@@ -1,8 +1,8 @@
 using System;
 using System.Collections.Generic;
+using System.IO;
 using Microsoft.ML;
 using Microsoft.ML.Transforms.TimeSeries;
-using Microsoft.ML.TimeSeries;
 
 namespace Samples.Dynamic
 {
@@ -16,8 +16,7 @@ public static void Example()
  // as well as the source of randomness.
  var ml = new MLContext();
 
- // Generate sample series data with a recurring pattern
- const int SeasonalitySize = 5;
+ // Generate sample series data with a recurring pattern.
  var data = new List<TimeSeriesData>()
  {
  new TimeSeriesData(0),
@@ -44,43 +43,58 @@ public static void Example()
 
  // Setup arguments.
  var inputColumnName = nameof(TimeSeriesData.Value);
+ var outputColumnName = nameof(ForecastResult.Forecast);
 
  // Instantiate the forecasting model.
- var model = ml.Forecasting.AdaptiveSingularSpectrumSequenceModeler(inputColumnName, data.Count, SeasonalitySize + 1, SeasonalitySize,
- 1, AdaptiveSingularSpectrumSequenceModeler.RankSelectionMethod.Exact, null, SeasonalitySize / 2, false, false);
+ var model = ml.Forecasting.ForecastBySsa(outputColumnName, inputColumnName, 5, 11, data.Count, 5);
 
  // Train.
- model.Train(dataView);
+var transformer = model.Fit(dataView);
 
  // Forecast next five values.
- var forecast = model.Forecast(5);
+ var forecastEngine = transformer.CreateTimeSeriesEngine<TimeSeriesData, ForecastResult>(ml);
+ var forecast = forecastEngine.Predict();
+
  Console.WriteLine($"Forecasted values:");
- Console.WriteLine("[{0}]", string.Join(", ", forecast));
+ Console.WriteLine("[{0}]", string.Join(", ", forecast.Forecast));
  // Forecasted values:
- // [2.452744, 2.589339, 2.729183, 2.873005, 3.028931]
+ // [1.977226, 1.020494, 1.760543, 3.437509, 4.266461]
 
  // Update with new observations.
- dataView = ml.Data.LoadFromEnumerable(new List<TimeSeriesData>() { new TimeSeriesData(0), new TimeSeriesData(0), new TimeSeriesData(0), new TimeSeriesData(0) });
- model.Update(dataView);
+ forecastEngine.Predict(new TimeSeriesData(0));
+ forecastEngine.Predict(new TimeSeriesData(0));
+ forecastEngine.Predict(new TimeSeriesData(0));
+ forecastEngine.Predict(new TimeSeriesData(0));
 
  // Checkpoint.
-ml.Model.SaveForecastingModel(model, "model.zip");
+forecastEngine.CheckPoint(ml, "model.zip");
 
  // Load the checkpointed model from disk.
- var modelCopy = ml.Model.LoadForecastingModel<float>("model.zip");
+ // Load the model.
+ ITransformer modelCopy;
+ using (var file = File.OpenRead("model.zip"))
+ modelCopy = ml.Model.Load(file, out DataViewSchema schema);
+
+ // We must create a new prediction engine from the persisted model.
+ var forecastEngineCopy = modelCopy.CreateTimeSeriesEngine<TimeSeriesData, ForecastResult>(ml);
 
  // Forecast with the checkpointed model loaded from disk.
- forecast = modelCopy.Forecast(5);
- Console.WriteLine("[{0}]", string.Join(", ", forecast));
- // [0.8681176, 0.8185108, 0.8069275, 0.84405, 0.9455081]
+ forecast = forecastEngineCopy.Predict();
+ Console.WriteLine("[{0}]", string.Join(", ", forecast.Forecast));
+ // [1.791331, 1.255525, 0.3060154, -0.200446, 0.5657795]
 
  // Forecast with the original model(that was checkpointed to disk).
- forecast = model.Forecast(5);
- Console.WriteLine("[{0}]", string.Join(", ", forecast));
- // [0.8681176, 0.8185108, 0.8069275, 0.84405, 0.9455081]
+ forecast = forecastEngine.Predict();
+ Console.WriteLine("[{0}]", string.Join(", ", forecast.Forecast));
+ // [1.791331, 1.255525, 0.3060154, -0.200446, 0.5657795]
 
  }
 
+ class ForecastResult
+ {
+ public float[] Forecast { get; set; }
+ }
+
  class TimeSeriesData
  {
  public float Value;

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/ForecastingWithConfidenceInterval.cs

@@ -2,7 +2,7 @@
 using System.Collections.Generic;
 using Microsoft.ML;
 using Microsoft.ML.Transforms.TimeSeries;
-using Microsoft.ML.TimeSeries;
+using System.IO;
 
 namespace Samples.Dynamic
 {
@@ -16,8 +16,7 @@ public static void Example()
  // as well as the source of randomness.
  var ml = new MLContext();
 
- // Generate sample series data with a recurring pattern
- const int SeasonalitySize = 5;
+ // Generate sample series data with a recurring pattern.
  var data = new List<TimeSeriesData>()
  {
  new TimeSeriesData(0),
@@ -44,50 +43,58 @@ public static void Example()
 
  // Setup arguments.
  var inputColumnName = nameof(TimeSeriesData.Value);
+ var outputColumnName = nameof(ForecastResult.Forecast);
 
- // Instantiate forecasting model.
- var model = ml.Forecasting.AdaptiveSingularSpectrumSequenceModeler(inputColumnName, data.Count, SeasonalitySize + 1, SeasonalitySize,
- 1, AdaptiveSingularSpectrumSequenceModeler.RankSelectionMethod.Exact, null, SeasonalitySize / 2, shouldComputeForecastIntervals: true, false);
+ // Instantiate the forecasting model.
+ var model = ml.Forecasting.ForecastBySsa(outputColumnName, inputColumnName, 5, 11, data.Count, 5,
+ confidenceLevel: 0.95f,
+ forcastingConfidentLowerBoundColumnName: "ConfidenceLowerBound",
+ forcastingConfidentUpperBoundColumnName: "ConfidenceUpperBound");
 
  // Train.
- model.Train(dataView);
-
- // Forecast next five values with confidence internal.
- float[] forecast;
- float[] confidenceIntervalLowerBounds;
- float[] confidenceIntervalUpperBounds;
- model.ForecastWithConfidenceIntervals(5, out forecast, out confidenceIntervalLowerBounds, out confidenceIntervalUpperBounds);
- PrintForecastValuesAndIntervals(forecast, confidenceIntervalLowerBounds, confidenceIntervalUpperBounds);
+ var transformer = model.Fit(dataView);
+
+ // Forecast next five values.
+ var forecastEngine = transformer.CreateTimeSeriesEngine<TimeSeriesData, ForecastResult>(ml);
+ var forecast = forecastEngine.Predict();
+
+ PrintForecastValuesAndIntervals(forecast.Forecast, forecast.ConfidenceLowerBound, forecast.ConfidenceUpperBound);
  // Forecasted values:
- // [2.452744, 2.589339, 2.729183, 2.873005, 3.028931]
+ // [1.977226, 1.020494, 1.760543, 3.437509, 4.266461]
  // Confidence intervals:
- // [-0.2235315 - 5.12902] [-0.08777174 - 5.266451] [0.05076938 - 5.407597] [0.1925406 - 5.553469] [0.3469928 - 5.71087]
+ // [0.3451088 - 3.609343] [-0.7967533 - 2.83774] [-0.058467 - 3.579552] [1.61505 - 5.259968] [2.349299 - 6.183623]
 
  // Update with new observations.
- dataView = ml.Data.LoadFromEnumerable(new List<TimeSeriesData>() { new TimeSeriesData(0), new TimeSeriesData(0), new TimeSeriesData(0), new TimeSeriesData(0) });
- model.Update(dataView);
+ forecastEngine.Predict(new TimeSeriesData(0));
+ forecastEngine.Predict(new TimeSeriesData(0));
+ forecastEngine.Predict(new TimeSeriesData(0));
+ forecastEngine.Predict(new TimeSeriesData(0));
 
  // Checkpoint.
-ml.Model.SaveForecastingModel(model, "model.zip");
+forecastEngine.CheckPoint(ml, "model.zip");
 
  // Load the checkpointed model from disk.
- var modelCopy = ml.Model.LoadForecastingModel<float>("model.zip");
+ // Load the model.
+ ITransformer modelCopy;
+ using (var file = File.OpenRead("model.zip"))
+ modelCopy = ml.Model.Load(file, out DataViewSchema schema);
+
+ // We must create a new prediction engine from the persisted model.
+ var forecastEngineCopy = modelCopy.CreateTimeSeriesEngine<TimeSeriesData, ForecastResult>(ml);
 
  // Forecast with the checkpointed model loaded from disk.
- modelCopy.ForecastWithConfidenceIntervals(5, out forecast, out confidenceIntervalLowerBounds, out confidenceIntervalUpperBounds);
- PrintForecastValuesAndIntervals(forecast, confidenceIntervalLowerBounds, confidenceIntervalUpperBounds);
- // Forecasted values:
- // [0.8681176, 0.8185108, 0.8069275, 0.84405, 0.9455081]
+ forecast = forecastEngineCopy.Predict();
+ PrintForecastValuesAndIntervals(forecast.Forecast, forecast.ConfidenceLowerBound, forecast.ConfidenceUpperBound);
+ // [1.791331, 1.255525, 0.3060154, -0.200446, 0.5657795]
  // Confidence intervals:
- // [-1.808158 - 3.544394] [-1.8586 - 3.495622] [-1.871486 - 3.485341] [-1.836414 - 3.524514] [-1.736431 - 3.627447]
+ // [0.1592142 - 3.423448] [-0.5617217 - 3.072772] [-1.512994 - 2.125025] [-2.022905 - 1.622013] [-1.351382 - 2.482941]
 
  // Forecast with the original model(that was checkpointed to disk).
- model.ForecastWithConfidenceIntervals(5, out forecast, out confidenceIntervalLowerBounds, out confidenceIntervalUpperBounds);
- PrintForecastValuesAndIntervals(forecast, confidenceIntervalLowerBounds, confidenceIntervalUpperBounds);
- // Forecasted values:
- // [0.8681176, 0.8185108, 0.8069275, 0.84405, 0.9455081]
+ forecast = forecastEngine.Predict();
+ PrintForecastValuesAndIntervals(forecast.Forecast, forecast.ConfidenceLowerBound, forecast.ConfidenceUpperBound);
+ // [1.791331, 1.255525, 0.3060154, -0.200446, 0.5657795]
  // Confidence intervals:
- // [-1.808158 - 3.544394] [-1.8586 - 3.495622] [-1.871486 - 3.485341] [-1.836414 - 3.524514] [-1.736431 - 3.627447]
+ // [0.1592142 - 3.423448] [-0.5617217 - 3.072772] [-1.512994 - 2.125025] [-2.022905 - 1.622013] [-1.351382 - 2.482941]
  }
 
  static void PrintForecastValuesAndIntervals(float[] forecast, float[] confidenceIntervalLowerBounds, float[] confidenceIntervalUpperBounds)
@@ -100,6 +107,13 @@ static void PrintForecastValuesAndIntervals(float[] forecast, float[] confidence
  Console.WriteLine();
  }
 
+ class ForecastResult
+ {
+ public float[] Forecast { get; set; }
+ public float[] ConfidenceLowerBound { get; set; }
+ public float[] ConfidenceUpperBound { get; set; }
+ }
+
  class TimeSeriesData
  {
  public float Value;

docs/samples/Microsoft.ML.Samples/Program.cs

@@ -1,5 +1,6 @@
 using System;
 using System.Reflection;
+using Samples.Dynamic;
 
 namespace Microsoft.ML.Samples
 {

docs/api-reference/io-time-series-ssa-forecast.md

@@ -0,0 +1,5 @@
+### Input and Output Columns
+There is only one input column.
+The input column must be <xref:System.Single> where a <xref:System.Single> value indicates a value at a timestamp in the time series.
+
+It produces either just one vector of forecasted values or three vectors: a vector of forecasted values, a vector of confidence lower bounds and a vector of confidence upper bounds.

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectAnomalyBySrCnn.cs

@@ -40,7 +40,7 @@ public static void Example()
  ITransformer model = ml.Transforms.DetectAnomalyBySrCnn(outputColumnName, inputColumnName, 16, 5, 5, 3, 8, 0.35).Fit(dataView);
 
  // Create a time series prediction engine from the model.
- var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, SrCnnAnomalyDetection>(ml);
+ var engine = model.CreateTimeSeriesEngine<TimeSeriesData, SrCnnAnomalyDetection>(ml);
 
  Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
  Console.WriteLine("Data\tAlert\tScore\tMag");

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsa.cs

@@ -57,7 +57,7 @@ public static void Example()
  ITransformer model = ml.Transforms.DetectChangePointBySsa(outputColumnName, inputColumnName, confidence, changeHistoryLength, TrainingSize, SeasonalitySize + 1).Fit(dataView);
 
  // Create a prediction engine from the model for feeding new data.
- var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
+ var engine = model.CreateTimeSeriesEngine<TimeSeriesData, ChangePointPrediction>(ml);
 
  // Start streaming new data points with no change point to the prediction engine.
  Console.WriteLine($"Output from ChangePoint predictions on new data:");
@@ -99,7 +99,7 @@ public static void Example()
  model = ml.Model.Load(file, out DataViewSchema schema);
 
  // We must create a new prediction engine from the persisted model.
- engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
+ engine = model.CreateTimeSeriesEngine<TimeSeriesData, ChangePointPrediction>(ml);
 
  // Run predictions on the loaded model.
  for (int i = 0; i < 5; i++)

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsaStream.cs

@@ -57,7 +57,7 @@ public static void Example()
  ITransformer model = ml.Transforms.DetectChangePointBySsa(outputColumnName, inputColumnName, confidence, changeHistoryLength, TrainingSize, SeasonalitySize + 1).Fit(dataView);
 
  // Create a prediction engine from the model for feeding new data.
- var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
+ var engine = model.CreateTimeSeriesEngine<TimeSeriesData, ChangePointPrediction>(ml);
 
  // Start streaming new data points with no change point to the prediction engine.
  Console.WriteLine($"Output from ChangePoint predictions on new data:");
@@ -103,7 +103,7 @@ public static void Example()
  model = ml.Model.Load(stream, out DataViewSchema schema);
 
  // We must create a new prediction engine from the persisted model.
- engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
+ engine = model.CreateTimeSeriesEngine<TimeSeriesData, ChangePointPrediction>(ml);
 
  // Run predictions on the loaded model.
  for (int i = 0; i < 5; i++)

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidChangePoint.cs

@@ -56,7 +56,7 @@ public static void Example()
  ITransformer model = ml.Transforms.DetectIidChangePoint(outputColumnName, inputColumnName, 95, Size / 4).Fit(dataView);
 
  // Create a time series prediction engine from the model.
- var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
+ var engine = model.CreateTimeSeriesEngine<TimeSeriesData, ChangePointPrediction>(ml);
 
  Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
  Console.WriteLine("Data\tAlert\tScore\tP-Value\tMartingale value");
@@ -97,7 +97,7 @@ public static void Example()
  model = ml.Model.Load(file, out DataViewSchema schema);
 
  // Create a time series prediction engine from the checkpointed model.
- engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
+ engine = model.CreateTimeSeriesEngine<TimeSeriesData, ChangePointPrediction>(ml);
  for (int index = 0; index < 8; index++)
  {
  // Anomaly change point detection.

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidSpike.cs

@@ -48,7 +48,7 @@ public static void Example()
  ITransformer model = ml.Transforms.DetectIidSpike(outputColumnName, inputColumnName, 95, Size).Fit(dataView);
 
  // Create a time series prediction engine from the model.
- var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, IidSpikePrediction>(ml);
+ var engine = model.CreateTimeSeriesEngine<TimeSeriesData, IidSpikePrediction>(ml);
 
  Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
  Console.WriteLine("Data\tAlert\tScore\tP-Value");

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectSpikeBySsa.cs

@@ -54,7 +54,7 @@ public static void Example()
  ITransformer model = ml.Transforms.DetectSpikeBySsa(outputColumnName, inputColumnName, 95, 8, TrainingSize, SeasonalitySize + 1).Fit(dataView);
 
  // Create a prediction engine from the model for feeding new data.
- var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, SsaSpikePrediction>(ml);
+ var engine = model.CreateTimeSeriesEngine<TimeSeriesData, SsaSpikePrediction>(ml);
 
  // Start streaming new data points with no change point to the prediction engine.
  Console.WriteLine($"Output from spike predictions on new data:");
@@ -94,7 +94,7 @@ public static void Example()
  model = ml.Model.Load(file, out DataViewSchema schema);
 
  // We must create a new prediction engine from the persisted model.
- engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, SsaSpikePrediction>(ml);
+ engine = model.CreateTimeSeriesEngine<TimeSeriesData, SsaSpikePrediction>(ml);
 
  // Run predictions on the loaded model.
  for (int i = 0; i < 5; i++)

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/Forecasting.cs

@@ -1,8 +1,8 @@
 using System;
 using System.Collections.Generic;
+using System.IO;
 using Microsoft.ML;
 using Microsoft.ML.Transforms.TimeSeries;
-using Microsoft.ML.TimeSeries;
 
 namespace Samples.Dynamic
 {
@@ -16,8 +16,7 @@ public static void Example()
  // as well as the source of randomness.
  var ml = new MLContext();
 
- // Generate sample series data with a recurring pattern
- const int SeasonalitySize = 5;
+ // Generate sample series data with a recurring pattern.
  var data = new List<TimeSeriesData>()
  {
  new TimeSeriesData(0),
@@ -44,43 +43,58 @@ public static void Example()
 
  // Setup arguments.
  var inputColumnName = nameof(TimeSeriesData.Value);
+ var outputColumnName = nameof(ForecastResult.Forecast);
 
  // Instantiate the forecasting model.
- var model = ml.Forecasting.AdaptiveSingularSpectrumSequenceModeler(inputColumnName, data.Count, SeasonalitySize + 1, SeasonalitySize,
- 1, AdaptiveSingularSpectrumSequenceModeler.RankSelectionMethod.Exact, null, SeasonalitySize / 2, false, false);
+ var model = ml.Forecasting.ForecastBySsa(outputColumnName, inputColumnName, 5, 11, data.Count, 5);
 
  // Train.
- model.Train(dataView);
+var transformer = model.Fit(dataView);
 
  // Forecast next five values.
- var forecast = model.Forecast(5);
+ var forecastEngine = transformer.CreateTimeSeriesEngine<TimeSeriesData, ForecastResult>(ml);
+ var forecast = forecastEngine.Predict();
+
  Console.WriteLine($"Forecasted values:");
- Console.WriteLine("[{0}]", string.Join(", ", forecast));
+ Console.WriteLine("[{0}]", string.Join(", ", forecast.Forecast));
  // Forecasted values:
- // [2.452744, 2.589339, 2.729183, 2.873005, 3.028931]
+ // [1.977226, 1.020494, 1.760543, 3.437509, 4.266461]
 
  // Update with new observations.
- dataView = ml.Data.LoadFromEnumerable(new List<TimeSeriesData>() { new TimeSeriesData(0), new TimeSeriesData(0), new TimeSeriesData(0), new TimeSeriesData(0) });
- model.Update(dataView);
+ forecastEngine.Predict(new TimeSeriesData(0));
+ forecastEngine.Predict(new TimeSeriesData(0));
+ forecastEngine.Predict(new TimeSeriesData(0));
+ forecastEngine.Predict(new TimeSeriesData(0));
 
  // Checkpoint.
-ml.Model.SaveForecastingModel(model, "model.zip");
+forecastEngine.CheckPoint(ml, "model.zip");
 
  // Load the checkpointed model from disk.
- var modelCopy = ml.Model.LoadForecastingModel<float>("model.zip");
+ // Load the model.
+ ITransformer modelCopy;
+ using (var file = File.OpenRead("model.zip"))
+ modelCopy = ml.Model.Load(file, out DataViewSchema schema);
+
+ // We must create a new prediction engine from the persisted model.
+ var forecastEngineCopy = modelCopy.CreateTimeSeriesEngine<TimeSeriesData, ForecastResult>(ml);
 
  // Forecast with the checkpointed model loaded from disk.
- forecast = modelCopy.Forecast(5);
- Console.WriteLine("[{0}]", string.Join(", ", forecast));
- // [0.8681176, 0.8185108, 0.8069275, 0.84405, 0.9455081]
+ forecast = forecastEngineCopy.Predict();
+ Console.WriteLine("[{0}]", string.Join(", ", forecast.Forecast));
+ // [1.791331, 1.255525, 0.3060154, -0.200446, 0.5657795]
 
  // Forecast with the original model(that was checkpointed to disk).
- forecast = model.Forecast(5);
- Console.WriteLine("[{0}]", string.Join(", ", forecast));
- // [0.8681176, 0.8185108, 0.8069275, 0.84405, 0.9455081]
+ forecast = forecastEngine.Predict();
+ Console.WriteLine("[{0}]", string.Join(", ", forecast.Forecast));
+ // [1.791331, 1.255525, 0.3060154, -0.200446, 0.5657795]
 
  }
 
+ class ForecastResult
+ {
+ public float[] Forecast { get; set; }
+ }
+
  class TimeSeriesData
  {
  public float Value;

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/ForecastingWithConfidenceInterval.cs

@@ -2,7 +2,7 @@
 using System.Collections.Generic;
 using Microsoft.ML;
 using Microsoft.ML.Transforms.TimeSeries;
-using Microsoft.ML.TimeSeries;
+using System.IO;
 
 namespace Samples.Dynamic
 {
@@ -16,8 +16,7 @@ public static void Example()
  // as well as the source of randomness.
  var ml = new MLContext();
 
- // Generate sample series data with a recurring pattern
- const int SeasonalitySize = 5;
+ // Generate sample series data with a recurring pattern.
  var data = new List<TimeSeriesData>()
  {
  new TimeSeriesData(0),
@@ -44,50 +43,58 @@ public static void Example()
 
  // Setup arguments.
  var inputColumnName = nameof(TimeSeriesData.Value);
+ var outputColumnName = nameof(ForecastResult.Forecast);
 
- // Instantiate forecasting model.
- var model = ml.Forecasting.AdaptiveSingularSpectrumSequenceModeler(inputColumnName, data.Count, SeasonalitySize + 1, SeasonalitySize,
- 1, AdaptiveSingularSpectrumSequenceModeler.RankSelectionMethod.Exact, null, SeasonalitySize / 2, shouldComputeForecastIntervals: true, false);
+ // Instantiate the forecasting model.
+ var model = ml.Forecasting.ForecastBySsa(outputColumnName, inputColumnName, 5, 11, data.Count, 5,
+ confidenceLevel: 0.95f,
+ forcastingConfidentLowerBoundColumnName: "ConfidenceLowerBound",
+ forcastingConfidentUpperBoundColumnName: "ConfidenceUpperBound");
 
  // Train.
- model.Train(dataView);
-
- // Forecast next five values with confidence internal.
- float[] forecast;
- float[] confidenceIntervalLowerBounds;
- float[] confidenceIntervalUpperBounds;
- model.ForecastWithConfidenceIntervals(5, out forecast, out confidenceIntervalLowerBounds, out confidenceIntervalUpperBounds);
- PrintForecastValuesAndIntervals(forecast, confidenceIntervalLowerBounds, confidenceIntervalUpperBounds);
+ var transformer = model.Fit(dataView);
+
+ // Forecast next five values.
+ var forecastEngine = transformer.CreateTimeSeriesEngine<TimeSeriesData, ForecastResult>(ml);
+ var forecast = forecastEngine.Predict();
+
+ PrintForecastValuesAndIntervals(forecast.Forecast, forecast.ConfidenceLowerBound, forecast.ConfidenceUpperBound);
  // Forecasted values:
- // [2.452744, 2.589339, 2.729183, 2.873005, 3.028931]
+ // [1.977226, 1.020494, 1.760543, 3.437509, 4.266461]
  // Confidence intervals:
- // [-0.2235315 - 5.12902] [-0.08777174 - 5.266451] [0.05076938 - 5.407597] [0.1925406 - 5.553469] [0.3469928 - 5.71087]
+ // [0.3451088 - 3.609343] [-0.7967533 - 2.83774] [-0.058467 - 3.579552] [1.61505 - 5.259968] [2.349299 - 6.183623]
 
  // Update with new observations.
- dataView = ml.Data.LoadFromEnumerable(new List<TimeSeriesData>() { new TimeSeriesData(0), new TimeSeriesData(0), new TimeSeriesData(0), new TimeSeriesData(0) });
- model.Update(dataView);
+ forecastEngine.Predict(new TimeSeriesData(0));
+ forecastEngine.Predict(new TimeSeriesData(0));
+ forecastEngine.Predict(new TimeSeriesData(0));
+ forecastEngine.Predict(new TimeSeriesData(0));
 
  // Checkpoint.
-ml.Model.SaveForecastingModel(model, "model.zip");
+forecastEngine.CheckPoint(ml, "model.zip");
 
  // Load the checkpointed model from disk.
- var modelCopy = ml.Model.LoadForecastingModel<float>("model.zip");
+ // Load the model.
+ ITransformer modelCopy;
+ using (var file = File.OpenRead("model.zip"))
+ modelCopy = ml.Model.Load(file, out DataViewSchema schema);
+
+ // We must create a new prediction engine from the persisted model.
+ var forecastEngineCopy = modelCopy.CreateTimeSeriesEngine<TimeSeriesData, ForecastResult>(ml);
 
  // Forecast with the checkpointed model loaded from disk.
- modelCopy.ForecastWithConfidenceIntervals(5, out forecast, out confidenceIntervalLowerBounds, out confidenceIntervalUpperBounds);
- PrintForecastValuesAndIntervals(forecast, confidenceIntervalLowerBounds, confidenceIntervalUpperBounds);
- // Forecasted values:
- // [0.8681176, 0.8185108, 0.8069275, 0.84405, 0.9455081]
+ forecast = forecastEngineCopy.Predict();
+ PrintForecastValuesAndIntervals(forecast.Forecast, forecast.ConfidenceLowerBound, forecast.ConfidenceUpperBound);
+ // [1.791331, 1.255525, 0.3060154, -0.200446, 0.5657795]
  // Confidence intervals:
- // [-1.808158 - 3.544394] [-1.8586 - 3.495622] [-1.871486 - 3.485341] [-1.836414 - 3.524514] [-1.736431 - 3.627447]
+ // [0.1592142 - 3.423448] [-0.5617217 - 3.072772] [-1.512994 - 2.125025] [-2.022905 - 1.622013] [-1.351382 - 2.482941]
 
  // Forecast with the original model(that was checkpointed to disk).
- model.ForecastWithConfidenceIntervals(5, out forecast, out confidenceIntervalLowerBounds, out confidenceIntervalUpperBounds);
- PrintForecastValuesAndIntervals(forecast, confidenceIntervalLowerBounds, confidenceIntervalUpperBounds);
- // Forecasted values:
- // [0.8681176, 0.8185108, 0.8069275, 0.84405, 0.9455081]
+ forecast = forecastEngine.Predict();
+ PrintForecastValuesAndIntervals(forecast.Forecast, forecast.ConfidenceLowerBound, forecast.ConfidenceUpperBound);
+ // [1.791331, 1.255525, 0.3060154, -0.200446, 0.5657795]
  // Confidence intervals:
- // [-1.808158 - 3.544394] [-1.8586 - 3.495622] [-1.871486 - 3.485341] [-1.836414 - 3.524514] [-1.736431 - 3.627447]
+ // [0.1592142 - 3.423448] [-0.5617217 - 3.072772] [-1.512994 - 2.125025] [-2.022905 - 1.622013] [-1.351382 - 2.482941]
  }
 
  static void PrintForecastValuesAndIntervals(float[] forecast, float[] confidenceIntervalLowerBounds, float[] confidenceIntervalUpperBounds)
@@ -100,6 +107,13 @@ static void PrintForecastValuesAndIntervals(float[] forecast, float[] confidence
  Console.WriteLine();
  }
 
+ class ForecastResult
+ {
+ public float[] Forecast { get; set; }
+ public float[] ConfidenceLowerBound { get; set; }
+ public float[] ConfidenceUpperBound { get; set; }
+ }
+
  class TimeSeriesData
  {
  public float Value;

docs/samples/Microsoft.ML.Samples/Program.cs

@@ -1,5 +1,6 @@
 using System;
 using System.Reflection;
+using Samples.Dynamic;
 
 namespace Microsoft.ML.Samples
 {