statsforecast

Forecasting using statistical models

Install

pip install statsforecast

How to use

import numpy as np
import pandas as pd
from IPython.display import display, Markdown

from statsforecast import StatsForecast
from statsforecast.models import random_walk_with_drift, seasonal_naive, ses

def display_df(df):
    display(Markdown(df.to_markdown()))

rng = np.random.RandomState(0)
serie1 = np.arange(1, 8)[np.arange(100) % 7] + rng.randint(-1, 2, size=100)
serie2 = np.arange(100) + rng.rand(100)
series = pd.DataFrame(
    {
        'ds': pd.date_range('2000-01-01', periods=serie1.size + serie2.size, freq='D'),
        'y': np.hstack([serie1, serie2]),
    },
    index=pd.Index([0] * serie1.size + [1] * serie2.size, name='unique_id')
)
display_df(pd.concat([series.head(), series.tail()]))

fcst = StatsForecast(series, models=[random_walk_with_drift, (seasonal_naive, 7), (ses, 0.1)], freq='D', n_jobs=2)
forecasts = fcst.forecast(5)
display_df(forecasts)

2021-12-08 20:33:06 statsforecast.core INFO: Computing forecasts
2021-12-08 20:33:07 statsforecast.core INFO: Computed forecasts for random_walk_with_drift.
2021-12-08 20:33:07 statsforecast.core INFO: Computed forecasts for seasonal_naive_season_length-7.
2021-12-08 20:33:07 statsforecast.core INFO: Computed forecasts for ses_alpha-0.1.