utilsforecast

utilsforecast

Install

PyPI

pip install utilsforecast

Conda

conda install -c conda-forge utilsforecast

How to use

Generate synthetic data

from utilsforecast.data import generate_series

series = generate_series(3, with_trend=True, static_as_categorical=False)
series

	unique_id	ds	y
0	0	2000-01-01	0.422133
1	0	2000-01-02	1.501407
2	0	2000-01-03	2.568495
3	0	2000-01-04	3.529085
4	0	2000-01-05	4.481929
...	...	...	...
481	2	2000-06-11	163.914625
482	2	2000-06-12	166.018479
483	2	2000-06-13	160.839176
484	2	2000-06-14	162.679603
485	2	2000-06-15	165.089288

486 rows × 3 columns

Plotting

from utilsforecast.plotting import plot_series

fig = plot_series(series, plot_random=False, max_insample_length=50, engine='matplotlib')
fig.savefig('imgs/index.png', bbox_inches='tight')

Preprocessing

from utilsforecast.preprocessing import fill_gaps

serie = series[series['unique_id'].eq(0)].tail(10)
# drop some points
with_gaps = serie.sample(frac=0.5, random_state=0).sort_values('ds')
with_gaps

	unique_id	ds	y
213	0	2000-08-01	18.543147
214	0	2000-08-02	19.941764
216	0	2000-08-04	21.968733
220	0	2000-08-08	19.091509
221	0	2000-08-09	20.220739

fill_gaps(with_gaps, freq='D')

	unique_id	ds	y
0	0	2000-08-01	18.543147
1	0	2000-08-02	19.941764
2	0	2000-08-03	NaN
3	0	2000-08-04	21.968733
4	0	2000-08-05	NaN
5	0	2000-08-06	NaN
6	0	2000-08-07	NaN
7	0	2000-08-08	19.091509
8	0	2000-08-09	20.220739

Evaluating

from functools import partial

import numpy as np

from utilsforecast.evaluation import evaluate
from utilsforecast.losses import mape, mase

valid = series.groupby('unique_id').tail(7).copy()
train = series.drop(valid.index)
rng = np.random.RandomState(0)
valid['seas_naive'] = train.groupby('unique_id')['y'].tail(7).values
valid['rand_model'] = valid['y'] * rng.rand(valid['y'].shape[0])
daily_mase = partial(mase, seasonality=7)
evaluate(valid, metrics=[mape, daily_mase], train_df=train)

	unique_id	metric	seas_naive	rand_model
0	0	mape	0.024139	0.440173
1	1	mape	0.054259	0.278123
2	2	mape	0.042642	0.480316
3	0	mase	0.907149	16.418014
4	1	mase	0.991635	6.404254
5	2	mase	1.013596	11.365040