Scalable machine learning based time series forecasting

python, forecast, forecasting, machine-learning, dask, lightgbm, time-series, xgboost
pip install mlforecast==0.13.0



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Machine Learning 🤖 Forecast

Scalable machine learning for time series forecasting

CI Python PyPi conda-forge License

mlforecast is a framework to perform time series forecasting using machine learning models, with the option to scale to massive amounts of data using remote clusters.



pip install mlforecast


conda install -c conda-forge mlforecast

For more detailed instructions you can refer to the installation page.

Quick Start

Get Started with this quick guide.

Follow this end-to-end walkthrough for best practices.

Sample notebooks


Current Python alternatives for machine learning models are slow, inaccurate and don’t scale well. So we created a library that can be used to forecast in production environments. MLForecast includes efficient feature engineering to train any machine learning model (with fit and predict methods such as sklearn) to fit millions of time series.


  • Fastest implementations of feature engineering for time series forecasting in Python.
  • Out-of-the-box compatibility with pandas, polars, spark, dask, and ray.
  • Probabilistic Forecasting with Conformal Prediction.
  • Support for exogenous variables and static covariates.
  • Familiar sklearn syntax: .fit and .predict.

Missing something? Please open an issue or write us in Slack

Examples and Guides

📚 End to End Walkthrough: model training, evaluation and selection for multiple time series.

🔎 Probabilistic Forecasting: use Conformal Prediction to produce prediciton intervals.

👩‍🔬 Cross Validation: robust model’s performance evaluation.

🔌 Predict Demand Peaks: electricity load forecasting for detecting daily peaks and reducing electric bills.

📈 Transfer Learning: pretrain a model using a set of time series and then predict another one using that pretrained model.

🌡️ Distributed Training: use a Dask, Ray or Spark cluster to train models at scale.

How to use

The following provides a very basic overview, for a more detailed description see the documentation.

Data setup

Store your time series in a pandas dataframe in long format, that is, each row represents an observation for a specific serie and timestamp.

from mlforecast.utils import generate_daily_series

series = generate_daily_series(
unique_id ds y static_0
0 id_00 2000-01-01 17.519167 72
1 id_00 2000-01-02 87.799695 72
2 id_00 2000-01-03 177.442975 72
3 id_00 2000-01-04 232.704110 72
4 id_00 2000-01-05 317.510474 72


Next define your models. These can be any regressor that follows the scikit-learn API.

import lightgbm as lgb
from sklearn.linear_model import LinearRegression
models = [
    lgb.LGBMRegressor(random_state=0, verbosity=-1),

Forecast object

Now instantiate an MLForecast object with the models and the features that you want to use. The features can be lags, transformations on the lags and date features. You can also define transformations to apply to the target before fitting, which will be restored when predicting.

from mlforecast import MLForecast
from mlforecast.lag_transforms import ExpandingMean, RollingMean
from mlforecast.target_transforms import Differences
fcst = MLForecast(
    lags=[7, 14],
        1: [ExpandingMean()],
        7: [RollingMean(window_size=28)]


To compute the features and train the models call fit on your Forecast object.
MLForecast(models=[LGBMRegressor, LinearRegression], freq=D, lag_features=['lag7', 'lag14', 'expanding_mean_lag1', 'rolling_mean_lag7_window_size28'], date_features=['dayofweek'], num_threads=1)


To get the forecasts for the next n days call predict(n) on the forecast object. This will automatically handle the updates required by the features using a recursive strategy.

predictions = fcst.predict(14)
unique_id ds LGBMRegressor LinearRegression
0 id_00 2000-04-04 299.923771 311.432371
1 id_00 2000-04-05 365.424147 379.466214
2 id_00 2000-04-06 432.562441 460.234028
3 id_00 2000-04-07 495.628000 524.278924
4 id_00 2000-04-08 60.786223 79.828767
... ... ... ... ...
275 id_19 2000-03-23 36.266780 28.333215
276 id_19 2000-03-24 44.370984 33.368228
277 id_19 2000-03-25 50.746222 38.613001
278 id_19 2000-03-26 58.906524 43.447398
279 id_19 2000-03-27 63.073949 48.666783

280 rows × 4 columns

Visualize results

from utilsforecast.plotting import plot_series
fig = plot_series(series, predictions, max_ids=4, plot_random=False)

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