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Disclaimer

This is a personal fork of timesfm, maintained to support custom changes for timecopilot and publish pypi wheels at timecopilot-timesfm. It may diverge from upstream.

Credits

This project is a fork of by Original Author(s).
All credit for the original code belongs to them. This fork is maintained independently to support TimeCopilot-specific changes.

TimesFM

TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting.

This open version is not an officially supported Google product.

Latest Model Version: TimesFM 2.5

Archived Model Versions:

  • 1.0 and 2.0: relevant code archived in the sub directory v1. You can pip install timesfm==1.3.0 to install an older version of this package to load them.

Update - Sept. 15, 2025

TimesFM 2.5 is out!

Comparing to TimesFM 2.0, this new 2.5 model:

  • uses 200M parameters, down from 500M.
  • supports up to 16k context length, up from 2048.
  • supports continuous quantile forecast up to 1k horizon via an optional 30M quantile head.
  • gets rid of the frequency indicator.
  • has a couple of new forecasting flags.

Along with the model upgrade we have also upgraded the inference API. This repo will be under construction over the next few weeks to

  1. add support for an upcoming Flax version of the model (faster inference).
  2. add back covariate support.
  3. populate more docstrings, docs and notebook.

Install

TODO(siriuz42): Package timesfm==2.0.0 and upload to PyPI .

Run

git clone https://github.com/google-research/timesfm.git
cd timesfm
pip install -e .

Code Example

importtorchimportnumpyasnpimporttimesfmtorch.set_float32_matmul_precision("high")
model=timesfm.TimesFM_2p5_200M_torch.from_pretrained("google/timesfm-2.5-200m-pytorch")
model.compile(
timesfm.ForecastConfig(
max_context=1024,
max_horizon=256,
normalize_inputs=True,
use_continuous_quantile_head=True,
force_flip_invariance=True,
infer_is_positive=True,
fix_quantile_crossing=True,
)
)
point_forecast, quantile_forecast=model.forecast(
horizon=12,
inputs=[
np.linspace(0, 1, 100),
np.sin(np.linspace(0, 20, 67)),
], # Two dummy inputs
)
point_forecast.shape# (2, 12)quantile_forecast.shape# (2, 12, 10): mean, then 10th to 90th quantiles.

About

TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting.

research.google/blog/a-decoder-only-foundation-model-for-time-series-forecasting/

Resources

Readme
License
Activity
Custom properties

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, 'i'); if (__m === '*' || __re.test(location.href)) { // Add copy buttons to all 
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function addCopyButtons() {
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var __re = new RegExp('^' + "github\\.com" + '
GitHub - TimeCopilot/timesfm: TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting. · GitHub
Skip to content
BranchesTags
Repository files navigation
Disclaimer
This is a personal fork of timesfm, maintained to support custom changes for timecopilot and publish pypi wheels at timecopilot-timesfm. It may diverge from upstream.
Credits
This project is a fork of by Original Author(s).

All credit for the original code belongs to them. This fork is maintained independently to support TimeCopilot-specific changes.
TimesFM
TimesFM (Time Series Foundation Model) is a pretrained time-series foundation
model developed by Google Research for time-series forecasting.
This open version is not an officially supported Google product.
Latest Model Version: TimesFM 2.5
Archived Model Versions:
  • 1.0 and 2.0: relevant code archived in the sub directory v1. You can pip install timesfm==1.3.0 to install an older version of this package to load
them.
Update - Sept. 15, 2025
TimesFM 2.5 is out!
Comparing to TimesFM 2.0, this new 2.5 model:
  • uses 200M parameters, down from 500M.
  • supports up to 16k context length, up from 2048.
  • supports continuous quantile forecast up to 1k horizon via an optional 30M
quantile head.
  • gets rid of the frequency indicator.
  • has a couple of new forecasting flags.
Along with the model upgrade we have also upgraded the inference API. This repo
will be under construction over the next few weeks to
  1. add support for an upcoming Flax version of the model (faster inference).
  2. add back covariate support.
  3. populate more docstrings, docs and notebook.
Install
TODO(siriuz42): Package timesfm==2.0.0 and upload to PyPI .
Run
git clone https://github.com/google-research/timesfm.git
cd timesfm
pip install -e .
Code Example
importtorchimportnumpyasnpimporttimesfmtorch.set_float32_matmul_precision("high")
model=timesfm.TimesFM_2p5_200M_torch.from_pretrained("google/timesfm-2.5-200m-pytorch")
model.compile(
timesfm.ForecastConfig(
max_context=1024,
max_horizon=256,
normalize_inputs=True,
use_continuous_quantile_head=True,
force_flip_invariance=True,
infer_is_positive=True,
fix_quantile_crossing=True,
)
)
point_forecast, quantile_forecast=model.forecast(
horizon=12,
inputs=[
np.linspace(0, 1, 100),
np.sin(np.linspace(0, 20, 67)),
], # Two dummy inputs
)
point_forecast.shape# (2, 12)quantile_forecast.shape# (2, 12, 10): mean, then 10th to 90th quantiles.
About
TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting.
research.google/blog/a-decoder-only-foundation-model-for-time-series-forecasting/
Resources
Readme
License
Activity
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0 watching
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, 'i');
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var __re = new RegExp('^' + ".*" + '
GitHub - TimeCopilot/timesfm: TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting. · GitHub
Skip to content
BranchesTags
Repository files navigation
Disclaimer
This is a personal fork of timesfm, maintained to support custom changes for timecopilot and publish pypi wheels at timecopilot-timesfm. It may diverge from upstream.
Credits
This project is a fork of by Original Author(s).

All credit for the original code belongs to them. This fork is maintained independently to support TimeCopilot-specific changes.
TimesFM
TimesFM (Time Series Foundation Model) is a pretrained time-series foundation
model developed by Google Research for time-series forecasting.
This open version is not an officially supported Google product.
Latest Model Version: TimesFM 2.5
Archived Model Versions:
  • 1.0 and 2.0: relevant code archived in the sub directory v1. You can pip install timesfm==1.3.0 to install an older version of this package to load
them.
Update - Sept. 15, 2025
TimesFM 2.5 is out!
Comparing to TimesFM 2.0, this new 2.5 model:
  • uses 200M parameters, down from 500M.
  • supports up to 16k context length, up from 2048.
  • supports continuous quantile forecast up to 1k horizon via an optional 30M
quantile head.
  • gets rid of the frequency indicator.
  • has a couple of new forecasting flags.
Along with the model upgrade we have also upgraded the inference API. This repo
will be under construction over the next few weeks to
  1. add support for an upcoming Flax version of the model (faster inference).
  2. add back covariate support.
  3. populate more docstrings, docs and notebook.
Install
TODO(siriuz42): Package timesfm==2.0.0 and upload to PyPI .
Run
git clone https://github.com/google-research/timesfm.git
cd timesfm
pip install -e .
Code Example
importtorchimportnumpyasnpimporttimesfmtorch.set_float32_matmul_precision("high")
model=timesfm.TimesFM_2p5_200M_torch.from_pretrained("google/timesfm-2.5-200m-pytorch")
model.compile(
timesfm.ForecastConfig(
max_context=1024,
max_horizon=256,
normalize_inputs=True,
use_continuous_quantile_head=True,
force_flip_invariance=True,
infer_is_positive=True,
fix_quantile_crossing=True,
)
)
point_forecast, quantile_forecast=model.forecast(
horizon=12,
inputs=[
np.linspace(0, 1, 100),
np.sin(np.linspace(0, 20, 67)),
], # Two dummy inputs
)
point_forecast.shape# (2, 12)quantile_forecast.shape# (2, 12, 10): mean, then 10th to 90th quantiles.
About
TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting.
research.google/blog/a-decoder-only-foundation-model-for-time-series-forecasting/
Resources
Readme
License
Activity
Custom properties
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
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var ref = document.referrer;
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} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
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(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - TimeCopilot/timesfm: TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting. · GitHub
Skip to content
BranchesTags
Repository files navigation
Disclaimer
This is a personal fork of timesfm, maintained to support custom changes for timecopilot and publish pypi wheels at timecopilot-timesfm. It may diverge from upstream.
Credits
This project is a fork of by Original Author(s).

All credit for the original code belongs to them. This fork is maintained independently to support TimeCopilot-specific changes.
TimesFM
TimesFM (Time Series Foundation Model) is a pretrained time-series foundation
model developed by Google Research for time-series forecasting.
This open version is not an officially supported Google product.
Latest Model Version: TimesFM 2.5
Archived Model Versions:
  • 1.0 and 2.0: relevant code archived in the sub directory v1. You can pip install timesfm==1.3.0 to install an older version of this package to load
them.
Update - Sept. 15, 2025
TimesFM 2.5 is out!
Comparing to TimesFM 2.0, this new 2.5 model:
  • uses 200M parameters, down from 500M.
  • supports up to 16k context length, up from 2048.
  • supports continuous quantile forecast up to 1k horizon via an optional 30M
quantile head.
  • gets rid of the frequency indicator.
  • has a couple of new forecasting flags.
Along with the model upgrade we have also upgraded the inference API. This repo
will be under construction over the next few weeks to
  1. add support for an upcoming Flax version of the model (faster inference).
  2. add back covariate support.
  3. populate more docstrings, docs and notebook.
Install
TODO(siriuz42): Package timesfm==2.0.0 and upload to PyPI .
Run
git clone https://github.com/google-research/timesfm.git
cd timesfm
pip install -e .
Code Example
importtorchimportnumpyasnpimporttimesfmtorch.set_float32_matmul_precision("high")
model=timesfm.TimesFM_2p5_200M_torch.from_pretrained("google/timesfm-2.5-200m-pytorch")
model.compile(
timesfm.ForecastConfig(
max_context=1024,
max_horizon=256,
normalize_inputs=True,
use_continuous_quantile_head=True,
force_flip_invariance=True,
infer_is_positive=True,
fix_quantile_crossing=True,
)
)
point_forecast, quantile_forecast=model.forecast(
horizon=12,
inputs=[
np.linspace(0, 1, 100),
np.sin(np.linspace(0, 20, 67)),
], # Two dummy inputs
)
point_forecast.shape# (2, 12)quantile_forecast.shape# (2, 12, 10): mean, then 10th to 90th quantiles.
About
TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting.
research.google/blog/a-decoder-only-foundation-model-for-time-series-forecasting/
Resources
Readme
License
Activity
Custom properties
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
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Packages
Contributors
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, 'i');
if (__m === '*' || __re.test(location.href)) {
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var changed = false;
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function cleanLinks() {
document.querySelectorAll('a[href]').forEach(function(a) {
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});
}
});
});
});
observer.observe(document.body, { childList: true, subtree: true });
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} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '
GitHub - TimeCopilot/timesfm: TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting. · GitHub
Skip to content
BranchesTags
Repository files navigation
Disclaimer
This is a personal fork of timesfm, maintained to support custom changes for timecopilot and publish pypi wheels at timecopilot-timesfm. It may diverge from upstream.
Credits
This project is a fork of by Original Author(s).

All credit for the original code belongs to them. This fork is maintained independently to support TimeCopilot-specific changes.
TimesFM
TimesFM (Time Series Foundation Model) is a pretrained time-series foundation
model developed by Google Research for time-series forecasting.
This open version is not an officially supported Google product.
Latest Model Version: TimesFM 2.5
Archived Model Versions:
  • 1.0 and 2.0: relevant code archived in the sub directory v1. You can pip install timesfm==1.3.0 to install an older version of this package to load
them.
Update - Sept. 15, 2025
TimesFM 2.5 is out!
Comparing to TimesFM 2.0, this new 2.5 model:
  • uses 200M parameters, down from 500M.
  • supports up to 16k context length, up from 2048.
  • supports continuous quantile forecast up to 1k horizon via an optional 30M
quantile head.
  • gets rid of the frequency indicator.
  • has a couple of new forecasting flags.
Along with the model upgrade we have also upgraded the inference API. This repo
will be under construction over the next few weeks to
  1. add support for an upcoming Flax version of the model (faster inference).
  2. add back covariate support.
  3. populate more docstrings, docs and notebook.
Install
TODO(siriuz42): Package timesfm==2.0.0 and upload to PyPI .
Run
git clone https://github.com/google-research/timesfm.git
cd timesfm
pip install -e .
Code Example
importtorchimportnumpyasnpimporttimesfmtorch.set_float32_matmul_precision("high")
model=timesfm.TimesFM_2p5_200M_torch.from_pretrained("google/timesfm-2.5-200m-pytorch")
model.compile(
timesfm.ForecastConfig(
max_context=1024,
max_horizon=256,
normalize_inputs=True,
use_continuous_quantile_head=True,
force_flip_invariance=True,
infer_is_positive=True,
fix_quantile_crossing=True,
)
)
point_forecast, quantile_forecast=model.forecast(
horizon=12,
inputs=[
np.linspace(0, 1, 100),
np.sin(np.linspace(0, 20, 67)),
], # Two dummy inputs
)
point_forecast.shape# (2, 12)quantile_forecast.shape# (2, 12, 10): mean, then 10th to 90th quantiles.
About
TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting.
research.google/blog/a-decoder-only-foundation-model-for-time-series-forecasting/
Resources
Readme
License
Activity
Custom properties
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0 watching
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0 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
// Auto-enable theater mode on YouTube
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var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]');
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(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - TimeCopilot/timesfm: TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting. · GitHub
Skip to content
BranchesTags
Repository files navigation
Disclaimer
This is a personal fork of timesfm, maintained to support custom changes for timecopilot and publish pypi wheels at timecopilot-timesfm. It may diverge from upstream.
Credits
This project is a fork of by Original Author(s).

All credit for the original code belongs to them. This fork is maintained independently to support TimeCopilot-specific changes.
TimesFM
TimesFM (Time Series Foundation Model) is a pretrained time-series foundation
model developed by Google Research for time-series forecasting.
This open version is not an officially supported Google product.
Latest Model Version: TimesFM 2.5
Archived Model Versions:
  • 1.0 and 2.0: relevant code archived in the sub directory v1. You can pip install timesfm==1.3.0 to install an older version of this package to load
them.
Update - Sept. 15, 2025
TimesFM 2.5 is out!
Comparing to TimesFM 2.0, this new 2.5 model:
  • uses 200M parameters, down from 500M.
  • supports up to 16k context length, up from 2048.
  • supports continuous quantile forecast up to 1k horizon via an optional 30M
quantile head.
  • gets rid of the frequency indicator.
  • has a couple of new forecasting flags.
Along with the model upgrade we have also upgraded the inference API. This repo
will be under construction over the next few weeks to
  1. add support for an upcoming Flax version of the model (faster inference).
  2. add back covariate support.
  3. populate more docstrings, docs and notebook.
Install
TODO(siriuz42): Package timesfm==2.0.0 and upload to PyPI .
Run
git clone https://github.com/google-research/timesfm.git
cd timesfm
pip install -e .
Code Example
importtorchimportnumpyasnpimporttimesfmtorch.set_float32_matmul_precision("high")
model=timesfm.TimesFM_2p5_200M_torch.from_pretrained("google/timesfm-2.5-200m-pytorch")
model.compile(
timesfm.ForecastConfig(
max_context=1024,
max_horizon=256,
normalize_inputs=True,
use_continuous_quantile_head=True,
force_flip_invariance=True,
infer_is_positive=True,
fix_quantile_crossing=True,
)
)
point_forecast, quantile_forecast=model.forecast(
horizon=12,
inputs=[
np.linspace(0, 1, 100),
np.sin(np.linspace(0, 20, 67)),
], # Two dummy inputs
)
point_forecast.shape# (2, 12)quantile_forecast.shape# (2, 12, 10): mean, then 10th to 90th quantiles.
About
TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting.
research.google/blog/a-decoder-only-foundation-model-for-time-series-forecasting/
Resources
Readme
License
Activity
Custom properties
Stars
0 stars
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0 watching
Forks
0 forks
Report repository
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Contributors
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, 'i');
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observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });
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try {
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GitHub - TimeCopilot/timesfm: TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting. · GitHub
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Disclaimer
This is a personal fork of timesfm, maintained to support custom changes for timecopilot and publish pypi wheels at timecopilot-timesfm. It may diverge from upstream.
Credits
This project is a fork of by Original Author(s).

All credit for the original code belongs to them. This fork is maintained independently to support TimeCopilot-specific changes.
TimesFM
TimesFM (Time Series Foundation Model) is a pretrained time-series foundation
model developed by Google Research for time-series forecasting.
This open version is not an officially supported Google product.
Latest Model Version: TimesFM 2.5
Archived Model Versions:
  • 1.0 and 2.0: relevant code archived in the sub directory v1. You can pip install timesfm==1.3.0 to install an older version of this package to load
them.
Update - Sept. 15, 2025
TimesFM 2.5 is out!
Comparing to TimesFM 2.0, this new 2.5 model:
  • uses 200M parameters, down from 500M.
  • supports up to 16k context length, up from 2048.
  • supports continuous quantile forecast up to 1k horizon via an optional 30M
quantile head.
  • gets rid of the frequency indicator.
  • has a couple of new forecasting flags.
Along with the model upgrade we have also upgraded the inference API. This repo
will be under construction over the next few weeks to
  1. add support for an upcoming Flax version of the model (faster inference).
  2. add back covariate support.
  3. populate more docstrings, docs and notebook.
Install
TODO(siriuz42): Package timesfm==2.0.0 and upload to PyPI .
Run
git clone https://github.com/google-research/timesfm.git
cd timesfm
pip install -e .
Code Example
importtorchimportnumpyasnpimporttimesfmtorch.set_float32_matmul_precision("high")
model=timesfm.TimesFM_2p5_200M_torch.from_pretrained("google/timesfm-2.5-200m-pytorch")
model.compile(
timesfm.ForecastConfig(
max_context=1024,
max_horizon=256,
normalize_inputs=True,
use_continuous_quantile_head=True,
force_flip_invariance=True,
infer_is_positive=True,
fix_quantile_crossing=True,
)
)
point_forecast, quantile_forecast=model.forecast(
horizon=12,
inputs=[
np.linspace(0, 1, 100),
np.sin(np.linspace(0, 20, 67)),
], # Two dummy inputs
)
point_forecast.shape# (2, 12)quantile_forecast.shape# (2, 12, 10): mean, then 10th to 90th quantiles.
About
TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting.
research.google/blog/a-decoder-only-foundation-model-for-time-series-forecasting/
Resources
Readme
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GitHub - TimeCopilot/timesfm: TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting. · GitHub
Skip to content
BranchesTags
Repository files navigation
Disclaimer
This is a personal fork of timesfm, maintained to support custom changes for timecopilot and publish pypi wheels at timecopilot-timesfm. It may diverge from upstream.
Credits
This project is a fork of by Original Author(s).

All credit for the original code belongs to them. This fork is maintained independently to support TimeCopilot-specific changes.
TimesFM
TimesFM (Time Series Foundation Model) is a pretrained time-series foundation
model developed by Google Research for time-series forecasting.
This open version is not an officially supported Google product.
Latest Model Version: TimesFM 2.5
Archived Model Versions:
  • 1.0 and 2.0: relevant code archived in the sub directory v1. You can pip install timesfm==1.3.0 to install an older version of this package to load
them.
Update - Sept. 15, 2025
TimesFM 2.5 is out!
Comparing to TimesFM 2.0, this new 2.5 model:
  • uses 200M parameters, down from 500M.
  • supports up to 16k context length, up from 2048.
  • supports continuous quantile forecast up to 1k horizon via an optional 30M
quantile head.
  • gets rid of the frequency indicator.
  • has a couple of new forecasting flags.
Along with the model upgrade we have also upgraded the inference API. This repo
will be under construction over the next few weeks to
  1. add support for an upcoming Flax version of the model (faster inference).
  2. add back covariate support.
  3. populate more docstrings, docs and notebook.
Install
TODO(siriuz42): Package timesfm==2.0.0 and upload to PyPI .
Run
git clone https://github.com/google-research/timesfm.git
cd timesfm
pip install -e .
Code Example
importtorchimportnumpyasnpimporttimesfmtorch.set_float32_matmul_precision("high")
model=timesfm.TimesFM_2p5_200M_torch.from_pretrained("google/timesfm-2.5-200m-pytorch")
model.compile(
timesfm.ForecastConfig(
max_context=1024,
max_horizon=256,
normalize_inputs=True,
use_continuous_quantile_head=True,
force_flip_invariance=True,
infer_is_positive=True,
fix_quantile_crossing=True,
)
)
point_forecast, quantile_forecast=model.forecast(
horizon=12,
inputs=[
np.linspace(0, 1, 100),
np.sin(np.linspace(0, 20, 67)),
], # Two dummy inputs
)
point_forecast.shape# (2, 12)quantile_forecast.shape# (2, 12, 10): mean, then 10th to 90th quantiles.
About
TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting.
research.google/blog/a-decoder-only-foundation-model-for-time-series-forecasting/
Resources
Readme
License
Activity
Custom properties
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages