mlforecast 0.2.0

Scalable machine learning based time series forecasting

mlforecast

Install

PyPI

pip install mlforecast

If you want to perform distributed training, you can instead use pip install mlforecast[distributed], which will also install dask. Note that you’ll also need to install either LightGBM or XGBoost.

conda-forge

conda install -c conda-forge mlforecast

Note that this installation comes with the required dependencies for the local interface. If you want to perform distributed training, you must install dask (conda install -c conda-forge dask) and either LightGBM or XGBoost.

How to use

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

Store your time series in a pandas dataframe with an index named unique_id that identifies each time serie, a column ds that contains the datestamps and a column y with the values.

from mlforecast.utils import generate_daily_series

series = generate_daily_series(20)
series.head()

	ds	y
unique_id
id_00	2000-01-01	0.264447
id_00	2000-01-02	1.284022
id_00	2000-01-03	2.462798
id_00	2000-01-04	3.035518
id_00	2000-01-05	4.043565

Then create a TimeSeries object with the features that you want to use. These include lags, transformations on the lags and date features. The lag transformations are defined as numba jitted functions that transform an array, if they have additional arguments you supply a tuple (transform_func, arg1, arg2, …).

from mlforecast.core import TimeSeries
from window_ops.expanding import expanding_mean
from window_ops.rolling import rolling_mean

ts = TimeSeries(
    lags=[7, 14],
    lag_transforms={
        1: [expanding_mean],
        7: [(rolling_mean, 7), (rolling_mean, 14)]
    },
    date_features=['dayofweek', 'month']
)
ts

TimeSeries(freq=<Day>, transforms=['lag-7', 'lag-14', 'expanding_mean_lag-1', 'rolling_mean_lag-7_window_size-7', 'rolling_mean_lag-7_window_size-14'], date_features=['dayofweek', 'month'], num_threads=1)

Next define a model. If you want to use the local interface this can be any regressor that follows the scikit-learn API. For distributed training there are LGBMForecast and XGBForecast.

from sklearn.ensemble import RandomForestRegressor

model = RandomForestRegressor(random_state=0)

Now instantiate your forecast object with the model and the time series. There are two types of forecasters, Forecast which is local and DistributedForecast which performs the whole process in a distributed way.

from mlforecast.forecast import Forecast

fcst = Forecast(model, ts)

To compute the features and train the model using them call .fit on your Forecast object.

fcst.fit(series)

Forecast(model=RandomForestRegressor(random_state=0), ts=TimeSeries(freq=<Day>, transforms=['lag-7', 'lag-14', 'expanding_mean_lag-1', 'rolling_mean_lag-7_window_size-7', 'rolling_mean_lag-7_window_size-14'], date_features=['dayofweek', 'month'], num_threads=1))

To get the forecasts for the next 14 days call .predict(14) on the forecaster. This will automatically handle the updates required by the features.

predictions = fcst.predict(14)
predictions.head()

	ds	y_pred
unique_id
id_00	2000-08-10	5.244840
id_00	2000-08-11	6.258609
id_00	2000-08-12	0.225484
id_00	2000-08-13	1.228957
id_00	2000-08-14	2.302455