Fix SR anomaly score calculation at beginning

src/Microsoft.ML.TimeSeries/SrCnnEntireAnomalyDetector.cs

@@ -309,6 +309,15 @@ public void Process()
  _previousBatch = _previousBatch.GetRange(_batch.Count, _bLen);
  _previousBatch.AddRange(_batch);
  _modeler.Train(_previousBatch.ToArray(), ref _results);
+
+ // move the values to front
+ for (int i = 0; i < _batch.Count; ++i)
+ {
+ for (int j = 0; j < _outputLength; ++j)
+ {
+ _results[i][j] = _results[_bLen + i][j];
+ }
+ }
  }
  else
  {
@@ -334,7 +343,7 @@ public ValueGetter<VBuffer<double>> CreateGetter(DataViewRowCursor input, string
  double src = default;
  srcGetter(ref src);
  var result = VBufferEditor.Create(ref dst, _outputLength);
- _results[input.Position % _batchSize + _bLen].CopyTo(result.Values);
+ _results[input.Position % _batchSize].CopyTo(result.Values);
  dst = result.Commit();
  };
  return getter;
@@ -351,6 +360,15 @@ internal sealed class SrCnnEntireModeler
  private static readonly double _deanomalyThreshold = 0.35;
  private static readonly double _boundSensitivity = 93.0;
  private static readonly double _unitForZero = 0.3;
+ private static readonly double _minimumScore = 0.0;
+ private static readonly double _maximumScore = 1.0;
+ // If the score window is smaller than this value, the anomaly score is tend to be small.
+ // Proof: For each point, the SR anomaly score is calculated as (w is average window size):
+ // (mag - avg_mag) / avg_mag
+ // = max (w * mag_{a} - sum_{i=0 to w-1} mag_{a - i}) / sum_{i=0 to w-1} mag_{a - i}
+ // = max ((w - 1) * mag_{a} + C) / (mag_{a} + C)
+ // <= w - 1
+ private static readonly int _minimumScoreWindowSize = (int)(_maximumScore * 10) + 1;
 
  // pseudo-code to generate the factors.
  // factors = []
@@ -577,15 +595,20 @@ private void SpectralResidual(double[] values, double[][] results, double thresh
  {
  _ifftMagList[i] = Math.Sqrt(_ifftRe[i] * _ifftRe[i] + _ifftIm[i] * _ifftIm[i]);
  }
+
  AverageFilter(_ifftMagList, Math.Min(_ifftMagList.Length, _judgementWindowSize));
+ for (int i = 0; i <= Math.Min(length, _minimumScoreWindowSize); ++i)
+ {
+ _cumSumList[i] = _cumSumList[Math.Min(length, _minimumScoreWindowSize) - 1];
+ }
 
  // Step 7: Calculate raw score and set result
  for (int i = 0; i < results.GetLength(0); ++i)
  {
  var score = CalculateScore(_ifftMagList[i], _cumSumList[i]);
  score /= 10.0f;
- score = Math.Min(score, 1);
- score = Math.Max(score, 0);
+ score = Math.Min(score, _maximumScore);
+ score = Math.Max(score, _minimumScore);
 
  var detres = score > threshold ? 1 : 0;
 

test/Microsoft.ML.TimeSeries.Tests/TimeSeriesDirectApi.cs

@@ -776,6 +776,64 @@ public void TestSrCnnAnomalyDetectorWithSeasonalAnomalyData(
  }
  }
 
+ [Theory, CombinatorialData]
+ public void TestSrCnnAnomalyDetectorWithAnomalyAtBeginning(
+ [CombinatorialValues(SrCnnDeseasonalityMode.Stl, SrCnnDeseasonalityMode.Mean, SrCnnDeseasonalityMode.Median)] SrCnnDeseasonalityMode mode
+ )
+ {
+ var ml = new MLContext(1);
+ IDataView dataView;
+ List<TimeSeriesDataDouble> data;
+
+ var dataPath = GetDataPath("Timeseries", "anomaly_at_beginning.csv");
+
+ // Load data from file into the dataView
+ dataView = ml.Data.LoadFromTextFile<TimeSeriesDataDouble>(dataPath, hasHeader: true);
+ data = ml.Data.CreateEnumerable<TimeSeriesDataDouble>(dataView, reuseRowObject: false).ToList();
+
+ // Setup the detection arguments
+ string outputColumnName = nameof(SrCnnAnomalyDetection.Prediction);
+ string inputColumnName = nameof(TimeSeriesDataDouble.Value);
+
+ // Do batch anomaly detection
+ var options = new SrCnnEntireAnomalyDetectorOptions()
+ {
+ Threshold = 0.30,
+ BatchSize = -1,
+ Sensitivity = 80.0,
+ DetectMode = SrCnnDetectMode.AnomalyAndMargin,
+ Period = 0,
+ DeseasonalityMode = mode
+ };
+
+ var outputDataView = ml.AnomalyDetection.DetectEntireAnomalyBySrCnn(dataView, outputColumnName, inputColumnName, options);
+
+ // Getting the data of the newly created column as an IEnumerable of SrCnnAnomalyDetection.
+ var predictionColumn = ml.Data.CreateEnumerable<SrCnnAnomalyDetection>(
+ outputDataView, reuseRowObject: false);
+
+ var anomalyIndex = 1;
+
+ int k = 0;
+ foreach (var prediction in predictionColumn)
+ {
+ Assert.Equal(7, prediction.Prediction.Length);
+ if (anomalyIndex == k)
+ {
+ Assert.Equal(1, prediction.Prediction[0]);
+ Assert.True(prediction.Prediction[6] > data[k].Value || data[k].Value > prediction.Prediction[5]);
+ }
+ else
+ {
+ Assert.Equal(0, prediction.Prediction[0]);
+ Assert.True(prediction.Prediction[6] <= data[k].Value);
+ Assert.True(data[k].Value <= prediction.Prediction[5]);
+ }
+
+ ++k;
+ }
+ }
+
  [Theory, CombinatorialData]
  public void TestSrcnnEntireDetectNonnegativeData(
  [CombinatorialValues(true, false)] bool isPositive)

test/data/Timeseries/anomaly_at_beginning.csv

@@ -0,0 +1,39 @@
+Value
+181.944
+37.176
+57.14
+67.128
+72.12
+77.112
+82.104
+83.1
+87.09
+92.088
+92.01
+97.08
+102.072
+107.05
+107.06
+117.048
+122.04
+132.024
+147
+151.82
+151.992
+151.72
+151.94
+156.969
+156.984
+156.92
+161.976
+161.94
+161.97
+166.968
+176.952
+181.94
+186.936
+201.91
+201.912
+201.9
+206.904
+216.88

src/Microsoft.ML.TimeSeries/SrCnnEntireAnomalyDetector.cs

@@ -309,6 +309,15 @@ public void Process()
  _previousBatch = _previousBatch.GetRange(_batch.Count, _bLen);
  _previousBatch.AddRange(_batch);
  _modeler.Train(_previousBatch.ToArray(), ref _results);
+
+ // move the values to front
+ for (int i = 0; i < _batch.Count; ++i)
+ {
+ for (int j = 0; j < _outputLength; ++j)
+ {
+ _results[i][j] = _results[_bLen + i][j];
+ }
+ }
  }
  else
  {
@@ -334,7 +343,7 @@ public ValueGetter<VBuffer<double>> CreateGetter(DataViewRowCursor input, string
  double src = default;
  srcGetter(ref src);
  var result = VBufferEditor.Create(ref dst, _outputLength);
- _results[input.Position % _batchSize + _bLen].CopyTo(result.Values);
+ _results[input.Position % _batchSize].CopyTo(result.Values);
  dst = result.Commit();
  };
  return getter;
@@ -351,6 +360,15 @@ internal sealed class SrCnnEntireModeler
  private static readonly double _deanomalyThreshold = 0.35;
  private static readonly double _boundSensitivity = 93.0;
  private static readonly double _unitForZero = 0.3;
+ private static readonly double _minimumScore = 0.0;
+ private static readonly double _maximumScore = 1.0;
+ // If the score window is smaller than this value, the anomaly score is tend to be small.
+ // Proof: For each point, the SR anomaly score is calculated as (w is average window size):
+ // (mag - avg_mag) / avg_mag
+ // = max (w * mag_{a} - sum_{i=0 to w-1} mag_{a - i}) / sum_{i=0 to w-1} mag_{a - i}
+ // = max ((w - 1) * mag_{a} + C) / (mag_{a} + C)
+ // <= w - 1
+ private static readonly int _minimumScoreWindowSize = (int)(_maximumScore * 10) + 1;
 
  // pseudo-code to generate the factors.
  // factors = []
@@ -577,15 +595,20 @@ private void SpectralResidual(double[] values, double[][] results, double thresh
  {
  _ifftMagList[i] = Math.Sqrt(_ifftRe[i] * _ifftRe[i] + _ifftIm[i] * _ifftIm[i]);
  }
+
  AverageFilter(_ifftMagList, Math.Min(_ifftMagList.Length, _judgementWindowSize));
+ for (int i = 0; i <= Math.Min(length, _minimumScoreWindowSize); ++i)
+ {
+ _cumSumList[i] = _cumSumList[Math.Min(length, _minimumScoreWindowSize) - 1];
+ }
 
  // Step 7: Calculate raw score and set result
  for (int i = 0; i < results.GetLength(0); ++i)
  {
  var score = CalculateScore(_ifftMagList[i], _cumSumList[i]);
  score /= 10.0f;
- score = Math.Min(score, 1);
- score = Math.Max(score, 0);
+ score = Math.Min(score, _maximumScore);
+ score = Math.Max(score, _minimumScore);
 
  var detres = score > threshold ? 1 : 0;
 

test/Microsoft.ML.TimeSeries.Tests/TimeSeriesDirectApi.cs

@@ -776,6 +776,64 @@ public void TestSrCnnAnomalyDetectorWithSeasonalAnomalyData(
  }
  }
 
+ [Theory, CombinatorialData]
+ public void TestSrCnnAnomalyDetectorWithAnomalyAtBeginning(
+ [CombinatorialValues(SrCnnDeseasonalityMode.Stl, SrCnnDeseasonalityMode.Mean, SrCnnDeseasonalityMode.Median)] SrCnnDeseasonalityMode mode
+ )
+ {
+ var ml = new MLContext(1);
+ IDataView dataView;
+ List<TimeSeriesDataDouble> data;
+
+ var dataPath = GetDataPath("Timeseries", "anomaly_at_beginning.csv");
+
+ // Load data from file into the dataView
+ dataView = ml.Data.LoadFromTextFile<TimeSeriesDataDouble>(dataPath, hasHeader: true);
+ data = ml.Data.CreateEnumerable<TimeSeriesDataDouble>(dataView, reuseRowObject: false).ToList();
+
+ // Setup the detection arguments
+ string outputColumnName = nameof(SrCnnAnomalyDetection.Prediction);
+ string inputColumnName = nameof(TimeSeriesDataDouble.Value);
+
+ // Do batch anomaly detection
+ var options = new SrCnnEntireAnomalyDetectorOptions()
+ {
+ Threshold = 0.30,
+ BatchSize = -1,
+ Sensitivity = 80.0,
+ DetectMode = SrCnnDetectMode.AnomalyAndMargin,
+ Period = 0,
+ DeseasonalityMode = mode
+ };
+
+ var outputDataView = ml.AnomalyDetection.DetectEntireAnomalyBySrCnn(dataView, outputColumnName, inputColumnName, options);
+
+ // Getting the data of the newly created column as an IEnumerable of SrCnnAnomalyDetection.
+ var predictionColumn = ml.Data.CreateEnumerable<SrCnnAnomalyDetection>(
+ outputDataView, reuseRowObject: false);
+
+ var anomalyIndex = 1;
+
+ int k = 0;
+ foreach (var prediction in predictionColumn)
+ {
+ Assert.Equal(7, prediction.Prediction.Length);
+ if (anomalyIndex == k)
+ {
+ Assert.Equal(1, prediction.Prediction[0]);
+ Assert.True(prediction.Prediction[6] > data[k].Value || data[k].Value > prediction.Prediction[5]);
+ }
+ else
+ {
+ Assert.Equal(0, prediction.Prediction[0]);
+ Assert.True(prediction.Prediction[6] <= data[k].Value);
+ Assert.True(data[k].Value <= prediction.Prediction[5]);
+ }
+
+ ++k;
+ }
+ }
+
  [Theory, CombinatorialData]
  public void TestSrcnnEntireDetectNonnegativeData(
  [CombinatorialValues(true, false)] bool isPositive)

test/data/Timeseries/anomaly_at_beginning.csv

@@ -0,0 +1,39 @@
+Value
+181.944
+37.176
+57.14
+67.128
+72.12
+77.112
+82.104
+83.1
+87.09
+92.088
+92.01
+97.08
+102.072
+107.05
+107.06
+117.048
+122.04
+132.024
+147
+151.82
+151.992
+151.72
+151.94
+156.969
+156.984
+156.92
+161.976
+161.94
+161.97
+166.968
+176.952
+181.94
+186.936
+201.91
+201.912
+201.9
+206.904
+216.88

src/Microsoft.ML.TimeSeries/SrCnnEntireAnomalyDetector.cs

@@ -309,6 +309,15 @@ public void Process()
  _previousBatch = _previousBatch.GetRange(_batch.Count, _bLen);
  _previousBatch.AddRange(_batch);
  _modeler.Train(_previousBatch.ToArray(), ref _results);
+
+ // move the values to front
+ for (int i = 0; i < _batch.Count; ++i)
+ {
+ for (int j = 0; j < _outputLength; ++j)
+ {
+ _results[i][j] = _results[_bLen + i][j];
+ }
+ }
  }
  else
  {
@@ -334,7 +343,7 @@ public ValueGetter<VBuffer<double>> CreateGetter(DataViewRowCursor input, string
  double src = default;
  srcGetter(ref src);
  var result = VBufferEditor.Create(ref dst, _outputLength);
- _results[input.Position % _batchSize + _bLen].CopyTo(result.Values);
+ _results[input.Position % _batchSize].CopyTo(result.Values);
  dst = result.Commit();
  };
  return getter;
@@ -351,6 +360,15 @@ internal sealed class SrCnnEntireModeler
  private static readonly double _deanomalyThreshold = 0.35;
  private static readonly double _boundSensitivity = 93.0;
  private static readonly double _unitForZero = 0.3;
+ private static readonly double _minimumScore = 0.0;
+ private static readonly double _maximumScore = 1.0;
+ // If the score window is smaller than this value, the anomaly score is tend to be small.
+ // Proof: For each point, the SR anomaly score is calculated as (w is average window size):
+ // (mag - avg_mag) / avg_mag
+ // = max (w * mag_{a} - sum_{i=0 to w-1} mag_{a - i}) / sum_{i=0 to w-1} mag_{a - i}
+ // = max ((w - 1) * mag_{a} + C) / (mag_{a} + C)
+ // <= w - 1
+ private static readonly int _minimumScoreWindowSize = (int)(_maximumScore * 10) + 1;
 
  // pseudo-code to generate the factors.
  // factors = []
@@ -577,15 +595,20 @@ private void SpectralResidual(double[] values, double[][] results, double thresh
  {
  _ifftMagList[i] = Math.Sqrt(_ifftRe[i] * _ifftRe[i] + _ifftIm[i] * _ifftIm[i]);
  }
+
  AverageFilter(_ifftMagList, Math.Min(_ifftMagList.Length, _judgementWindowSize));
+ for (int i = 0; i <= Math.Min(length, _minimumScoreWindowSize); ++i)
+ {
+ _cumSumList[i] = _cumSumList[Math.Min(length, _minimumScoreWindowSize) - 1];
+ }
 
  // Step 7: Calculate raw score and set result
  for (int i = 0; i < results.GetLength(0); ++i)
  {
  var score = CalculateScore(_ifftMagList[i], _cumSumList[i]);
  score /= 10.0f;
- score = Math.Min(score, 1);
- score = Math.Max(score, 0);
+ score = Math.Min(score, _maximumScore);
+ score = Math.Max(score, _minimumScore);
 
  var detres = score > threshold ? 1 : 0;
 

test/Microsoft.ML.TimeSeries.Tests/TimeSeriesDirectApi.cs

@@ -776,6 +776,64 @@ public void TestSrCnnAnomalyDetectorWithSeasonalAnomalyData(
  }
  }
 
+ [Theory, CombinatorialData]
+ public void TestSrCnnAnomalyDetectorWithAnomalyAtBeginning(
+ [CombinatorialValues(SrCnnDeseasonalityMode.Stl, SrCnnDeseasonalityMode.Mean, SrCnnDeseasonalityMode.Median)] SrCnnDeseasonalityMode mode
+ )
+ {
+ var ml = new MLContext(1);
+ IDataView dataView;
+ List<TimeSeriesDataDouble> data;
+
+ var dataPath = GetDataPath("Timeseries", "anomaly_at_beginning.csv");
+
+ // Load data from file into the dataView
+ dataView = ml.Data.LoadFromTextFile<TimeSeriesDataDouble>(dataPath, hasHeader: true);
+ data = ml.Data.CreateEnumerable<TimeSeriesDataDouble>(dataView, reuseRowObject: false).ToList();
+
+ // Setup the detection arguments
+ string outputColumnName = nameof(SrCnnAnomalyDetection.Prediction);
+ string inputColumnName = nameof(TimeSeriesDataDouble.Value);
+
+ // Do batch anomaly detection
+ var options = new SrCnnEntireAnomalyDetectorOptions()
+ {
+ Threshold = 0.30,
+ BatchSize = -1,
+ Sensitivity = 80.0,
+ DetectMode = SrCnnDetectMode.AnomalyAndMargin,
+ Period = 0,
+ DeseasonalityMode = mode
+ };
+
+ var outputDataView = ml.AnomalyDetection.DetectEntireAnomalyBySrCnn(dataView, outputColumnName, inputColumnName, options);
+
+ // Getting the data of the newly created column as an IEnumerable of SrCnnAnomalyDetection.
+ var predictionColumn = ml.Data.CreateEnumerable<SrCnnAnomalyDetection>(
+ outputDataView, reuseRowObject: false);
+
+ var anomalyIndex = 1;
+
+ int k = 0;
+ foreach (var prediction in predictionColumn)
+ {
+ Assert.Equal(7, prediction.Prediction.Length);
+ if (anomalyIndex == k)
+ {
+ Assert.Equal(1, prediction.Prediction[0]);
+ Assert.True(prediction.Prediction[6] > data[k].Value || data[k].Value > prediction.Prediction[5]);
+ }
+ else
+ {
+ Assert.Equal(0, prediction.Prediction[0]);
+ Assert.True(prediction.Prediction[6] <= data[k].Value);
+ Assert.True(data[k].Value <= prediction.Prediction[5]);
+ }
+
+ ++k;
+ }
+ }
+
  [Theory, CombinatorialData]
  public void TestSrcnnEntireDetectNonnegativeData(
  [CombinatorialValues(true, false)] bool isPositive)

test/data/Timeseries/anomaly_at_beginning.csv

@@ -0,0 +1,39 @@
+Value
+181.944
+37.176
+57.14
+67.128
+72.12
+77.112
+82.104
+83.1
+87.09
+92.088
+92.01
+97.08
+102.072
+107.05
+107.06
+117.048
+122.04
+132.024
+147
+151.82
+151.992
+151.72
+151.94
+156.969
+156.984
+156.92
+161.976
+161.94
+161.97
+166.968
+176.952
+181.94
+186.936
+201.91
+201.912
+201.9
+206.904
+216.88

src/Microsoft.ML.TimeSeries/SrCnnEntireAnomalyDetector.cs

@@ -309,6 +309,15 @@ public void Process()
  _previousBatch = _previousBatch.GetRange(_batch.Count, _bLen);
  _previousBatch.AddRange(_batch);
  _modeler.Train(_previousBatch.ToArray(), ref _results);
+
+ // move the values to front
+ for (int i = 0; i < _batch.Count; ++i)
+ {
+ for (int j = 0; j < _outputLength; ++j)
+ {
+ _results[i][j] = _results[_bLen + i][j];
+ }
+ }
  }
  else
  {
@@ -334,7 +343,7 @@ public ValueGetter<VBuffer<double>> CreateGetter(DataViewRowCursor input, string
  double src = default;
  srcGetter(ref src);
  var result = VBufferEditor.Create(ref dst, _outputLength);
- _results[input.Position % _batchSize + _bLen].CopyTo(result.Values);
+ _results[input.Position % _batchSize].CopyTo(result.Values);
  dst = result.Commit();
  };
  return getter;
@@ -351,6 +360,15 @@ internal sealed class SrCnnEntireModeler
  private static readonly double _deanomalyThreshold = 0.35;
  private static readonly double _boundSensitivity = 93.0;
  private static readonly double _unitForZero = 0.3;
+ private static readonly double _minimumScore = 0.0;
+ private static readonly double _maximumScore = 1.0;
+ // If the score window is smaller than this value, the anomaly score is tend to be small.
+ // Proof: For each point, the SR anomaly score is calculated as (w is average window size):
+ // (mag - avg_mag) / avg_mag
+ // = max (w * mag_{a} - sum_{i=0 to w-1} mag_{a - i}) / sum_{i=0 to w-1} mag_{a - i}
+ // = max ((w - 1) * mag_{a} + C) / (mag_{a} + C)
+ // <= w - 1
+ private static readonly int _minimumScoreWindowSize = (int)(_maximumScore * 10) + 1;
 
  // pseudo-code to generate the factors.
  // factors = []
@@ -577,15 +595,20 @@ private void SpectralResidual(double[] values, double[][] results, double thresh
  {
  _ifftMagList[i] = Math.Sqrt(_ifftRe[i] * _ifftRe[i] + _ifftIm[i] * _ifftIm[i]);
  }
+
  AverageFilter(_ifftMagList, Math.Min(_ifftMagList.Length, _judgementWindowSize));
+ for (int i = 0; i <= Math.Min(length, _minimumScoreWindowSize); ++i)
+ {
+ _cumSumList[i] = _cumSumList[Math.Min(length, _minimumScoreWindowSize) - 1];
+ }
 
  // Step 7: Calculate raw score and set result
  for (int i = 0; i < results.GetLength(0); ++i)
  {
  var score = CalculateScore(_ifftMagList[i], _cumSumList[i]);
  score /= 10.0f;
- score = Math.Min(score, 1);
- score = Math.Max(score, 0);
+ score = Math.Min(score, _maximumScore);
+ score = Math.Max(score, _minimumScore);
 
  var detres = score > threshold ? 1 : 0;
 

test/Microsoft.ML.TimeSeries.Tests/TimeSeriesDirectApi.cs

@@ -776,6 +776,64 @@ public void TestSrCnnAnomalyDetectorWithSeasonalAnomalyData(
  }
  }
 
+ [Theory, CombinatorialData]
+ public void TestSrCnnAnomalyDetectorWithAnomalyAtBeginning(
+ [CombinatorialValues(SrCnnDeseasonalityMode.Stl, SrCnnDeseasonalityMode.Mean, SrCnnDeseasonalityMode.Median)] SrCnnDeseasonalityMode mode
+ )
+ {
+ var ml = new MLContext(1);
+ IDataView dataView;
+ List<TimeSeriesDataDouble> data;
+
+ var dataPath = GetDataPath("Timeseries", "anomaly_at_beginning.csv");
+
+ // Load data from file into the dataView
+ dataView = ml.Data.LoadFromTextFile<TimeSeriesDataDouble>(dataPath, hasHeader: true);
+ data = ml.Data.CreateEnumerable<TimeSeriesDataDouble>(dataView, reuseRowObject: false).ToList();
+
+ // Setup the detection arguments
+ string outputColumnName = nameof(SrCnnAnomalyDetection.Prediction);
+ string inputColumnName = nameof(TimeSeriesDataDouble.Value);
+
+ // Do batch anomaly detection
+ var options = new SrCnnEntireAnomalyDetectorOptions()
+ {
+ Threshold = 0.30,
+ BatchSize = -1,
+ Sensitivity = 80.0,
+ DetectMode = SrCnnDetectMode.AnomalyAndMargin,
+ Period = 0,
+ DeseasonalityMode = mode
+ };
+
+ var outputDataView = ml.AnomalyDetection.DetectEntireAnomalyBySrCnn(dataView, outputColumnName, inputColumnName, options);
+
+ // Getting the data of the newly created column as an IEnumerable of SrCnnAnomalyDetection.
+ var predictionColumn = ml.Data.CreateEnumerable<SrCnnAnomalyDetection>(
+ outputDataView, reuseRowObject: false);
+
+ var anomalyIndex = 1;
+
+ int k = 0;
+ foreach (var prediction in predictionColumn)
+ {
+ Assert.Equal(7, prediction.Prediction.Length);
+ if (anomalyIndex == k)
+ {
+ Assert.Equal(1, prediction.Prediction[0]);
+ Assert.True(prediction.Prediction[6] > data[k].Value || data[k].Value > prediction.Prediction[5]);
+ }
+ else
+ {
+ Assert.Equal(0, prediction.Prediction[0]);
+ Assert.True(prediction.Prediction[6] <= data[k].Value);
+ Assert.True(data[k].Value <= prediction.Prediction[5]);
+ }
+
+ ++k;
+ }
+ }
+
  [Theory, CombinatorialData]
  public void TestSrcnnEntireDetectNonnegativeData(
  [CombinatorialValues(true, false)] bool isPositive)

test/data/Timeseries/anomaly_at_beginning.csv

@@ -0,0 +1,39 @@
+Value
+181.944
+37.176
+57.14
+67.128
+72.12
+77.112
+82.104
+83.1
+87.09
+92.088
+92.01
+97.08
+102.072
+107.05
+107.06
+117.048
+122.04
+132.024
+147
+151.82
+151.992
+151.72
+151.94
+156.969
+156.984
+156.92
+161.976
+161.94
+161.97
+166.968
+176.952
+181.94
+186.936
+201.91
+201.912
+201.9
+206.904
+216.88

src/Microsoft.ML.TimeSeries/SrCnnEntireAnomalyDetector.cs

@@ -309,6 +309,15 @@ public void Process()
  _previousBatch = _previousBatch.GetRange(_batch.Count, _bLen);
  _previousBatch.AddRange(_batch);
  _modeler.Train(_previousBatch.ToArray(), ref _results);
+
+ // move the values to front
+ for (int i = 0; i < _batch.Count; ++i)
+ {
+ for (int j = 0; j < _outputLength; ++j)
+ {
+ _results[i][j] = _results[_bLen + i][j];
+ }
+ }
  }
  else
  {
@@ -334,7 +343,7 @@ public ValueGetter<VBuffer<double>> CreateGetter(DataViewRowCursor input, string
  double src = default;
  srcGetter(ref src);
  var result = VBufferEditor.Create(ref dst, _outputLength);
- _results[input.Position % _batchSize + _bLen].CopyTo(result.Values);
+ _results[input.Position % _batchSize].CopyTo(result.Values);
  dst = result.Commit();
  };
  return getter;
@@ -351,6 +360,15 @@ internal sealed class SrCnnEntireModeler
  private static readonly double _deanomalyThreshold = 0.35;
  private static readonly double _boundSensitivity = 93.0;
  private static readonly double _unitForZero = 0.3;
+ private static readonly double _minimumScore = 0.0;
+ private static readonly double _maximumScore = 1.0;
+ // If the score window is smaller than this value, the anomaly score is tend to be small.
+ // Proof: For each point, the SR anomaly score is calculated as (w is average window size):
+ // (mag - avg_mag) / avg_mag
+ // = max (w * mag_{a} - sum_{i=0 to w-1} mag_{a - i}) / sum_{i=0 to w-1} mag_{a - i}
+ // = max ((w - 1) * mag_{a} + C) / (mag_{a} + C)
+ // <= w - 1
+ private static readonly int _minimumScoreWindowSize = (int)(_maximumScore * 10) + 1;
 
  // pseudo-code to generate the factors.
  // factors = []
@@ -577,15 +595,20 @@ private void SpectralResidual(double[] values, double[][] results, double thresh
  {
  _ifftMagList[i] = Math.Sqrt(_ifftRe[i] * _ifftRe[i] + _ifftIm[i] * _ifftIm[i]);
  }
+
  AverageFilter(_ifftMagList, Math.Min(_ifftMagList.Length, _judgementWindowSize));
+ for (int i = 0; i <= Math.Min(length, _minimumScoreWindowSize); ++i)
+ {
+ _cumSumList[i] = _cumSumList[Math.Min(length, _minimumScoreWindowSize) - 1];
+ }
 
  // Step 7: Calculate raw score and set result
  for (int i = 0; i < results.GetLength(0); ++i)
  {
  var score = CalculateScore(_ifftMagList[i], _cumSumList[i]);
  score /= 10.0f;
- score = Math.Min(score, 1);
- score = Math.Max(score, 0);
+ score = Math.Min(score, _maximumScore);
+ score = Math.Max(score, _minimumScore);
 
  var detres = score > threshold ? 1 : 0;
 

test/Microsoft.ML.TimeSeries.Tests/TimeSeriesDirectApi.cs

@@ -776,6 +776,64 @@ public void TestSrCnnAnomalyDetectorWithSeasonalAnomalyData(
  }
  }
 
+ [Theory, CombinatorialData]
+ public void TestSrCnnAnomalyDetectorWithAnomalyAtBeginning(
+ [CombinatorialValues(SrCnnDeseasonalityMode.Stl, SrCnnDeseasonalityMode.Mean, SrCnnDeseasonalityMode.Median)] SrCnnDeseasonalityMode mode
+ )
+ {
+ var ml = new MLContext(1);
+ IDataView dataView;
+ List<TimeSeriesDataDouble> data;
+
+ var dataPath = GetDataPath("Timeseries", "anomaly_at_beginning.csv");
+
+ // Load data from file into the dataView
+ dataView = ml.Data.LoadFromTextFile<TimeSeriesDataDouble>(dataPath, hasHeader: true);
+ data = ml.Data.CreateEnumerable<TimeSeriesDataDouble>(dataView, reuseRowObject: false).ToList();
+
+ // Setup the detection arguments
+ string outputColumnName = nameof(SrCnnAnomalyDetection.Prediction);
+ string inputColumnName = nameof(TimeSeriesDataDouble.Value);
+
+ // Do batch anomaly detection
+ var options = new SrCnnEntireAnomalyDetectorOptions()
+ {
+ Threshold = 0.30,
+ BatchSize = -1,
+ Sensitivity = 80.0,
+ DetectMode = SrCnnDetectMode.AnomalyAndMargin,
+ Period = 0,
+ DeseasonalityMode = mode
+ };
+
+ var outputDataView = ml.AnomalyDetection.DetectEntireAnomalyBySrCnn(dataView, outputColumnName, inputColumnName, options);
+
+ // Getting the data of the newly created column as an IEnumerable of SrCnnAnomalyDetection.
+ var predictionColumn = ml.Data.CreateEnumerable<SrCnnAnomalyDetection>(
+ outputDataView, reuseRowObject: false);
+
+ var anomalyIndex = 1;
+
+ int k = 0;
+ foreach (var prediction in predictionColumn)
+ {
+ Assert.Equal(7, prediction.Prediction.Length);
+ if (anomalyIndex == k)
+ {
+ Assert.Equal(1, prediction.Prediction[0]);
+ Assert.True(prediction.Prediction[6] > data[k].Value || data[k].Value > prediction.Prediction[5]);
+ }
+ else
+ {
+ Assert.Equal(0, prediction.Prediction[0]);
+ Assert.True(prediction.Prediction[6] <= data[k].Value);
+ Assert.True(data[k].Value <= prediction.Prediction[5]);
+ }
+
+ ++k;
+ }
+ }
+
  [Theory, CombinatorialData]
  public void TestSrcnnEntireDetectNonnegativeData(
  [CombinatorialValues(true, false)] bool isPositive)

test/data/Timeseries/anomaly_at_beginning.csv

@@ -0,0 +1,39 @@
+Value
+181.944
+37.176
+57.14
+67.128
+72.12
+77.112
+82.104
+83.1
+87.09
+92.088
+92.01
+97.08
+102.072
+107.05
+107.06
+117.048
+122.04
+132.024
+147
+151.82
+151.992
+151.72
+151.94
+156.969
+156.984
+156.92
+161.976
+161.94
+161.97
+166.968
+176.952
+181.94
+186.936
+201.91
+201.912
+201.9
+206.904
+216.88

src/Microsoft.ML.TimeSeries/SrCnnEntireAnomalyDetector.cs

@@ -309,6 +309,15 @@ public void Process()
  _previousBatch = _previousBatch.GetRange(_batch.Count, _bLen);
  _previousBatch.AddRange(_batch);
  _modeler.Train(_previousBatch.ToArray(), ref _results);
+
+ // move the values to front
+ for (int i = 0; i < _batch.Count; ++i)
+ {
+ for (int j = 0; j < _outputLength; ++j)
+ {
+ _results[i][j] = _results[_bLen + i][j];
+ }
+ }
  }
  else
  {
@@ -334,7 +343,7 @@ public ValueGetter<VBuffer<double>> CreateGetter(DataViewRowCursor input, string
  double src = default;
  srcGetter(ref src);
  var result = VBufferEditor.Create(ref dst, _outputLength);
- _results[input.Position % _batchSize + _bLen].CopyTo(result.Values);
+ _results[input.Position % _batchSize].CopyTo(result.Values);
  dst = result.Commit();
  };
  return getter;
@@ -351,6 +360,15 @@ internal sealed class SrCnnEntireModeler
  private static readonly double _deanomalyThreshold = 0.35;
  private static readonly double _boundSensitivity = 93.0;
  private static readonly double _unitForZero = 0.3;
+ private static readonly double _minimumScore = 0.0;
+ private static readonly double _maximumScore = 1.0;
+ // If the score window is smaller than this value, the anomaly score is tend to be small.
+ // Proof: For each point, the SR anomaly score is calculated as (w is average window size):
+ // (mag - avg_mag) / avg_mag
+ // = max (w * mag_{a} - sum_{i=0 to w-1} mag_{a - i}) / sum_{i=0 to w-1} mag_{a - i}
+ // = max ((w - 1) * mag_{a} + C) / (mag_{a} + C)
+ // <= w - 1
+ private static readonly int _minimumScoreWindowSize = (int)(_maximumScore * 10) + 1;
 
  // pseudo-code to generate the factors.
  // factors = []
@@ -577,15 +595,20 @@ private void SpectralResidual(double[] values, double[][] results, double thresh
  {
  _ifftMagList[i] = Math.Sqrt(_ifftRe[i] * _ifftRe[i] + _ifftIm[i] * _ifftIm[i]);
  }
+
  AverageFilter(_ifftMagList, Math.Min(_ifftMagList.Length, _judgementWindowSize));
+ for (int i = 0; i <= Math.Min(length, _minimumScoreWindowSize); ++i)
+ {
+ _cumSumList[i] = _cumSumList[Math.Min(length, _minimumScoreWindowSize) - 1];
+ }
 
  // Step 7: Calculate raw score and set result
  for (int i = 0; i < results.GetLength(0); ++i)
  {
  var score = CalculateScore(_ifftMagList[i], _cumSumList[i]);
  score /= 10.0f;
- score = Math.Min(score, 1);
- score = Math.Max(score, 0);
+ score = Math.Min(score, _maximumScore);
+ score = Math.Max(score, _minimumScore);
 
  var detres = score > threshold ? 1 : 0;
 

test/Microsoft.ML.TimeSeries.Tests/TimeSeriesDirectApi.cs

@@ -776,6 +776,64 @@ public void TestSrCnnAnomalyDetectorWithSeasonalAnomalyData(
  }
  }
 
+ [Theory, CombinatorialData]
+ public void TestSrCnnAnomalyDetectorWithAnomalyAtBeginning(
+ [CombinatorialValues(SrCnnDeseasonalityMode.Stl, SrCnnDeseasonalityMode.Mean, SrCnnDeseasonalityMode.Median)] SrCnnDeseasonalityMode mode
+ )
+ {
+ var ml = new MLContext(1);
+ IDataView dataView;
+ List<TimeSeriesDataDouble> data;
+
+ var dataPath = GetDataPath("Timeseries", "anomaly_at_beginning.csv");
+
+ // Load data from file into the dataView
+ dataView = ml.Data.LoadFromTextFile<TimeSeriesDataDouble>(dataPath, hasHeader: true);
+ data = ml.Data.CreateEnumerable<TimeSeriesDataDouble>(dataView, reuseRowObject: false).ToList();
+
+ // Setup the detection arguments
+ string outputColumnName = nameof(SrCnnAnomalyDetection.Prediction);
+ string inputColumnName = nameof(TimeSeriesDataDouble.Value);
+
+ // Do batch anomaly detection
+ var options = new SrCnnEntireAnomalyDetectorOptions()
+ {
+ Threshold = 0.30,
+ BatchSize = -1,
+ Sensitivity = 80.0,
+ DetectMode = SrCnnDetectMode.AnomalyAndMargin,
+ Period = 0,
+ DeseasonalityMode = mode
+ };
+
+ var outputDataView = ml.AnomalyDetection.DetectEntireAnomalyBySrCnn(dataView, outputColumnName, inputColumnName, options);
+
+ // Getting the data of the newly created column as an IEnumerable of SrCnnAnomalyDetection.
+ var predictionColumn = ml.Data.CreateEnumerable<SrCnnAnomalyDetection>(
+ outputDataView, reuseRowObject: false);
+
+ var anomalyIndex = 1;
+
+ int k = 0;
+ foreach (var prediction in predictionColumn)
+ {
+ Assert.Equal(7, prediction.Prediction.Length);
+ if (anomalyIndex == k)
+ {
+ Assert.Equal(1, prediction.Prediction[0]);
+ Assert.True(prediction.Prediction[6] > data[k].Value || data[k].Value > prediction.Prediction[5]);
+ }
+ else
+ {
+ Assert.Equal(0, prediction.Prediction[0]);
+ Assert.True(prediction.Prediction[6] <= data[k].Value);
+ Assert.True(data[k].Value <= prediction.Prediction[5]);
+ }
+
+ ++k;
+ }
+ }
+
  [Theory, CombinatorialData]
  public void TestSrcnnEntireDetectNonnegativeData(
  [CombinatorialValues(true, false)] bool isPositive)

test/data/Timeseries/anomaly_at_beginning.csv

@@ -0,0 +1,39 @@
+Value
+181.944
+37.176
+57.14
+67.128
+72.12
+77.112
+82.104
+83.1
+87.09
+92.088
+92.01
+97.08
+102.072
+107.05
+107.06
+117.048
+122.04
+132.024
+147
+151.82
+151.992
+151.72
+151.94
+156.969
+156.984
+156.92
+161.976
+161.94
+161.97
+166.968
+176.952
+181.94
+186.936
+201.91
+201.912
+201.9
+206.904
+216.88

src/Microsoft.ML.TimeSeries/SrCnnEntireAnomalyDetector.cs

@@ -309,6 +309,15 @@ public void Process()
  _previousBatch = _previousBatch.GetRange(_batch.Count, _bLen);
  _previousBatch.AddRange(_batch);
  _modeler.Train(_previousBatch.ToArray(), ref _results);
+
+ // move the values to front
+ for (int i = 0; i < _batch.Count; ++i)
+ {
+ for (int j = 0; j < _outputLength; ++j)
+ {
+ _results[i][j] = _results[_bLen + i][j];
+ }
+ }
  }
  else
  {
@@ -334,7 +343,7 @@ public ValueGetter<VBuffer<double>> CreateGetter(DataViewRowCursor input, string
  double src = default;
  srcGetter(ref src);
  var result = VBufferEditor.Create(ref dst, _outputLength);
- _results[input.Position % _batchSize + _bLen].CopyTo(result.Values);
+ _results[input.Position % _batchSize].CopyTo(result.Values);
  dst = result.Commit();
  };
  return getter;
@@ -351,6 +360,15 @@ internal sealed class SrCnnEntireModeler
  private static readonly double _deanomalyThreshold = 0.35;
  private static readonly double _boundSensitivity = 93.0;
  private static readonly double _unitForZero = 0.3;
+ private static readonly double _minimumScore = 0.0;
+ private static readonly double _maximumScore = 1.0;
+ // If the score window is smaller than this value, the anomaly score is tend to be small.
+ // Proof: For each point, the SR anomaly score is calculated as (w is average window size):
+ // (mag - avg_mag) / avg_mag
+ // = max (w * mag_{a} - sum_{i=0 to w-1} mag_{a - i}) / sum_{i=0 to w-1} mag_{a - i}
+ // = max ((w - 1) * mag_{a} + C) / (mag_{a} + C)
+ // <= w - 1
+ private static readonly int _minimumScoreWindowSize = (int)(_maximumScore * 10) + 1;
 
  // pseudo-code to generate the factors.
  // factors = []
@@ -577,15 +595,20 @@ private void SpectralResidual(double[] values, double[][] results, double thresh
  {
  _ifftMagList[i] = Math.Sqrt(_ifftRe[i] * _ifftRe[i] + _ifftIm[i] * _ifftIm[i]);
  }
+
  AverageFilter(_ifftMagList, Math.Min(_ifftMagList.Length, _judgementWindowSize));
+ for (int i = 0; i <= Math.Min(length, _minimumScoreWindowSize); ++i)
+ {
+ _cumSumList[i] = _cumSumList[Math.Min(length, _minimumScoreWindowSize) - 1];
+ }
 
  // Step 7: Calculate raw score and set result
  for (int i = 0; i < results.GetLength(0); ++i)
  {
  var score = CalculateScore(_ifftMagList[i], _cumSumList[i]);
  score /= 10.0f;
- score = Math.Min(score, 1);
- score = Math.Max(score, 0);
+ score = Math.Min(score, _maximumScore);
+ score = Math.Max(score, _minimumScore);
 
  var detres = score > threshold ? 1 : 0;
 

test/Microsoft.ML.TimeSeries.Tests/TimeSeriesDirectApi.cs

@@ -776,6 +776,64 @@ public void TestSrCnnAnomalyDetectorWithSeasonalAnomalyData(
  }
  }
 
+ [Theory, CombinatorialData]
+ public void TestSrCnnAnomalyDetectorWithAnomalyAtBeginning(
+ [CombinatorialValues(SrCnnDeseasonalityMode.Stl, SrCnnDeseasonalityMode.Mean, SrCnnDeseasonalityMode.Median)] SrCnnDeseasonalityMode mode
+ )
+ {
+ var ml = new MLContext(1);
+ IDataView dataView;
+ List<TimeSeriesDataDouble> data;
+
+ var dataPath = GetDataPath("Timeseries", "anomaly_at_beginning.csv");
+
+ // Load data from file into the dataView
+ dataView = ml.Data.LoadFromTextFile<TimeSeriesDataDouble>(dataPath, hasHeader: true);
+ data = ml.Data.CreateEnumerable<TimeSeriesDataDouble>(dataView, reuseRowObject: false).ToList();
+
+ // Setup the detection arguments
+ string outputColumnName = nameof(SrCnnAnomalyDetection.Prediction);
+ string inputColumnName = nameof(TimeSeriesDataDouble.Value);
+
+ // Do batch anomaly detection
+ var options = new SrCnnEntireAnomalyDetectorOptions()
+ {
+ Threshold = 0.30,
+ BatchSize = -1,
+ Sensitivity = 80.0,
+ DetectMode = SrCnnDetectMode.AnomalyAndMargin,
+ Period = 0,
+ DeseasonalityMode = mode
+ };
+
+ var outputDataView = ml.AnomalyDetection.DetectEntireAnomalyBySrCnn(dataView, outputColumnName, inputColumnName, options);
+
+ // Getting the data of the newly created column as an IEnumerable of SrCnnAnomalyDetection.
+ var predictionColumn = ml.Data.CreateEnumerable<SrCnnAnomalyDetection>(
+ outputDataView, reuseRowObject: false);
+
+ var anomalyIndex = 1;
+
+ int k = 0;
+ foreach (var prediction in predictionColumn)
+ {
+ Assert.Equal(7, prediction.Prediction.Length);
+ if (anomalyIndex == k)
+ {
+ Assert.Equal(1, prediction.Prediction[0]);
+ Assert.True(prediction.Prediction[6] > data[k].Value || data[k].Value > prediction.Prediction[5]);
+ }
+ else
+ {
+ Assert.Equal(0, prediction.Prediction[0]);
+ Assert.True(prediction.Prediction[6] <= data[k].Value);
+ Assert.True(data[k].Value <= prediction.Prediction[5]);
+ }
+
+ ++k;
+ }
+ }
+
  [Theory, CombinatorialData]
  public void TestSrcnnEntireDetectNonnegativeData(
  [CombinatorialValues(true, false)] bool isPositive)

test/data/Timeseries/anomaly_at_beginning.csv

@@ -0,0 +1,39 @@
+Value
+181.944
+37.176
+57.14
+67.128
+72.12
+77.112
+82.104
+83.1
+87.09
+92.088
+92.01
+97.08
+102.072
+107.05
+107.06
+117.048
+122.04
+132.024
+147
+151.82
+151.992
+151.72
+151.94
+156.969
+156.984
+156.92
+161.976
+161.94
+161.97
+166.968
+176.952
+181.94
+186.936
+201.91
+201.912
+201.9
+206.904
+216.88

src/Microsoft.ML.TimeSeries/SrCnnEntireAnomalyDetector.cs

@@ -309,6 +309,15 @@ public void Process()
  _previousBatch = _previousBatch.GetRange(_batch.Count, _bLen);
  _previousBatch.AddRange(_batch);
  _modeler.Train(_previousBatch.ToArray(), ref _results);
+
+ // move the values to front
+ for (int i = 0; i < _batch.Count; ++i)
+ {
+ for (int j = 0; j < _outputLength; ++j)
+ {
+ _results[i][j] = _results[_bLen + i][j];
+ }
+ }
  }
  else
  {
@@ -334,7 +343,7 @@ public ValueGetter<VBuffer<double>> CreateGetter(DataViewRowCursor input, string
  double src = default;
  srcGetter(ref src);
  var result = VBufferEditor.Create(ref dst, _outputLength);
- _results[input.Position % _batchSize + _bLen].CopyTo(result.Values);
+ _results[input.Position % _batchSize].CopyTo(result.Values);
  dst = result.Commit();
  };
  return getter;
@@ -351,6 +360,15 @@ internal sealed class SrCnnEntireModeler
  private static readonly double _deanomalyThreshold = 0.35;
  private static readonly double _boundSensitivity = 93.0;
  private static readonly double _unitForZero = 0.3;
+ private static readonly double _minimumScore = 0.0;
+ private static readonly double _maximumScore = 1.0;
+ // If the score window is smaller than this value, the anomaly score is tend to be small.
+ // Proof: For each point, the SR anomaly score is calculated as (w is average window size):
+ // (mag - avg_mag) / avg_mag
+ // = max (w * mag_{a} - sum_{i=0 to w-1} mag_{a - i}) / sum_{i=0 to w-1} mag_{a - i}
+ // = max ((w - 1) * mag_{a} + C) / (mag_{a} + C)
+ // <= w - 1
+ private static readonly int _minimumScoreWindowSize = (int)(_maximumScore * 10) + 1;
 
  // pseudo-code to generate the factors.
  // factors = []
@@ -577,15 +595,20 @@ private void SpectralResidual(double[] values, double[][] results, double thresh
  {
  _ifftMagList[i] = Math.Sqrt(_ifftRe[i] * _ifftRe[i] + _ifftIm[i] * _ifftIm[i]);
  }
+
  AverageFilter(_ifftMagList, Math.Min(_ifftMagList.Length, _judgementWindowSize));
+ for (int i = 0; i <= Math.Min(length, _minimumScoreWindowSize); ++i)
+ {
+ _cumSumList[i] = _cumSumList[Math.Min(length, _minimumScoreWindowSize) - 1];
+ }
 
  // Step 7: Calculate raw score and set result
  for (int i = 0; i < results.GetLength(0); ++i)
  {
  var score = CalculateScore(_ifftMagList[i], _cumSumList[i]);
  score /= 10.0f;
- score = Math.Min(score, 1);
- score = Math.Max(score, 0);
+ score = Math.Min(score, _maximumScore);
+ score = Math.Max(score, _minimumScore);
 
  var detres = score > threshold ? 1 : 0;
 

test/Microsoft.ML.TimeSeries.Tests/TimeSeriesDirectApi.cs

@@ -776,6 +776,64 @@ public void TestSrCnnAnomalyDetectorWithSeasonalAnomalyData(
  }
  }
 
+ [Theory, CombinatorialData]
+ public void TestSrCnnAnomalyDetectorWithAnomalyAtBeginning(
+ [CombinatorialValues(SrCnnDeseasonalityMode.Stl, SrCnnDeseasonalityMode.Mean, SrCnnDeseasonalityMode.Median)] SrCnnDeseasonalityMode mode
+ )
+ {
+ var ml = new MLContext(1);
+ IDataView dataView;
+ List<TimeSeriesDataDouble> data;
+
+ var dataPath = GetDataPath("Timeseries", "anomaly_at_beginning.csv");
+
+ // Load data from file into the dataView
+ dataView = ml.Data.LoadFromTextFile<TimeSeriesDataDouble>(dataPath, hasHeader: true);
+ data = ml.Data.CreateEnumerable<TimeSeriesDataDouble>(dataView, reuseRowObject: false).ToList();
+
+ // Setup the detection arguments
+ string outputColumnName = nameof(SrCnnAnomalyDetection.Prediction);
+ string inputColumnName = nameof(TimeSeriesDataDouble.Value);
+
+ // Do batch anomaly detection
+ var options = new SrCnnEntireAnomalyDetectorOptions()
+ {
+ Threshold = 0.30,
+ BatchSize = -1,
+ Sensitivity = 80.0,
+ DetectMode = SrCnnDetectMode.AnomalyAndMargin,
+ Period = 0,
+ DeseasonalityMode = mode
+ };
+
+ var outputDataView = ml.AnomalyDetection.DetectEntireAnomalyBySrCnn(dataView, outputColumnName, inputColumnName, options);
+
+ // Getting the data of the newly created column as an IEnumerable of SrCnnAnomalyDetection.
+ var predictionColumn = ml.Data.CreateEnumerable<SrCnnAnomalyDetection>(
+ outputDataView, reuseRowObject: false);
+
+ var anomalyIndex = 1;
+
+ int k = 0;
+ foreach (var prediction in predictionColumn)
+ {
+ Assert.Equal(7, prediction.Prediction.Length);
+ if (anomalyIndex == k)
+ {
+ Assert.Equal(1, prediction.Prediction[0]);
+ Assert.True(prediction.Prediction[6] > data[k].Value || data[k].Value > prediction.Prediction[5]);
+ }
+ else
+ {
+ Assert.Equal(0, prediction.Prediction[0]);
+ Assert.True(prediction.Prediction[6] <= data[k].Value);
+ Assert.True(data[k].Value <= prediction.Prediction[5]);
+ }
+
+ ++k;
+ }
+ }
+
  [Theory, CombinatorialData]
  public void TestSrcnnEntireDetectNonnegativeData(
  [CombinatorialValues(true, false)] bool isPositive)

test/data/Timeseries/anomaly_at_beginning.csv

@@ -0,0 +1,39 @@
+Value
+181.944
+37.176
+57.14
+67.128
+72.12
+77.112
+82.104
+83.1
+87.09
+92.088
+92.01
+97.08
+102.072
+107.05
+107.06
+117.048
+122.04
+132.024
+147
+151.82
+151.992
+151.72
+151.94
+156.969
+156.984
+156.92
+161.976
+161.94
+161.97
+166.968
+176.952
+181.94
+186.936
+201.91
+201.912
+201.9
+206.904
+216.88