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Forecasting utilities

Project description

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

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