StatsForecast works on top of Spark, Dask, and Ray through Fugue. StatsForecast will read the input DataFrame and use the corresponding engine. For example, if the input is a Spark DataFrame, StatsForecast will use the existing Spark session to run the forecast.

​
Installation

As long as Dask is installed and configured, StatsForecast will be able to use it. If executing on a distributed Dask cluster, make use the statsforecast library is installed across all the workers.

​
StatsForecast on Pandas

Before running on Dask, it’s recommended to test on a smaller Pandas dataset to make sure everything is working. This example also helps show the small differences when using Dask. 
from statsforecast.core import StatsForecast
from statsforecast.models import ( 
    AutoARIMA,
    AutoETS,
)
from statsforecast.utils import generate_series

n_series = 4
horizon = 7

series = generate_series(n_series)

sf = StatsForecast(
    models=[AutoETS(season_length=7)],
    freq='D',
)
sf.forecast(df=series, h=horizon).head()
unique_iddsAutoETS
002000-08-105.261609
102000-08-116.196357
202000-08-120.282309
302000-08-131.264195
402000-08-142.262453

​
Executing on Dask

To run the forecasts distributed on Dask, just pass in a Dask DataFrame instead. 
import dask.dataframe as dd

series['unique_id'] = series['unique_id'].astype(str)
ddf = dd.from_pandas(series, npartitions=4)
sf.forecast(df=ddf, h=horizon).compute().head()
unique_iddsAutoETS
002000-08-10 00:00:005.261609
102000-08-11 00:00:006.196357
202000-08-12 00:00:000.282309
302000-08-13 00:00:001.264195
402000-08-14 00:00:002.262453