statsforecast 0.2.0

Time series forecasting suite using statistical models

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()]))

unique_id	ds	y
0	2000-01-01 00:00:00	0
0	2000-01-02 00:00:00	2
0	2000-01-03 00:00:00	2
0	2000-01-04 00:00:00	4
0	2000-01-05 00:00:00	5
1	2000-07-14 00:00:00	95.7649
1	2000-07-15 00:00:00	96.9441
1	2000-07-16 00:00:00	97.75
1	2000-07-17 00:00:00	98.3394
1	2000-07-18 00:00:00	99.4895

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.

unique_id	ds	random_walk_with_drift	seasonal_naive_season_length-7	ses_alpha-0.1
0	2000-04-10 00:00:00	3.0303	3	3.85506
0	2000-04-11 00:00:00	3.06061	5	3.85506
0	2000-04-12 00:00:00	3.09091	4	3.85506
0	2000-04-13 00:00:00	3.12121	7	3.85506
0	2000-04-14 00:00:00	3.15152	6	3.85506
1	2000-07-19 00:00:00	100.489	93.0166	90.4709
1	2000-07-20 00:00:00	101.489	94.2307	90.4709
1	2000-07-21 00:00:00	102.489	95.7649	90.4709
1	2000-07-22 00:00:00	103.489	96.9441	90.4709
1	2000-07-23 00:00:00	104.489	97.75	90.4709