Forecasting utilities
Project description
utilsforecast
Install
pip install 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
plot(series, plot_random=False, max_insample_length=50, engine='matplotlib')
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 |
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