mlforecast 1.0.31

Scalable machine learning based time series forecasting

mlforecast

Machine Learning 🤖 Forecast

Scalable machine learning for time series forecasting

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.

Install

PyPI

pip install mlforecast

conda-forge

conda install -c conda-forge mlforecast

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

Quick Start

1. Get Started with this quick guide.
2. Follow this end-to-end walkthrough for best practices.

Videos

• Overview

Sample notebooks

• m5
• m5-polars
• m4
• m4-cv
• favorita
• VN1

Why?

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.

Features

• 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

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(
    n_series=20,
    max_length=100,
    n_static_features=1,
    static_as_categorical=False,
    with_trend=True
)
series.head()

	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

Note: The unique_id serves as an identifier for each distinct time series in your dataset. If you are using only single time series from your dataset, set this column to a constant value.

Models

Next define your models, each one will be trained on all series. 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),
    LinearRegression(),
]

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(
    models=models,
    freq='D',
    lags=[7, 14],
    lag_transforms={
        1: [ExpandingMean()],
        7: [RollingMean(window_size=28)]
    },
    date_features=['dayofweek'],
    target_transforms=[Differences([1])],
)

Training

To compute the features and train the models call fit on your Forecast object.

fcst.fit(series)

MLForecast(models=[LGBMRegressor, LinearRegression], freq=D, lag_features=['lag7', 'lag14', 'expanding_mean_lag1', 'rolling_mean_lag7_window_size28'], date_features=['dayofweek'], num_threads=1)

Predicting

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)
predictions

	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)

How to contribute

See CONTRIBUTING.md.