Utils | MLForecast

from fastcore.test import test_eq, test_fail
from nbdev import show_doc

generate_daily_series

generate_daily_series(n_series, min_length=50, max_length=500, n_static_features=0, equal_ends=False, static_as_categorical=True, with_trend=False, seed=0, engine='pandas')

Generate Synthetic Panel Series.

Parameters:

Name	Type	Description	Default
n_series	int	Number of series for synthetic panel.	required
min_length	int, default=50	Minimum length of synthetic panel’s series.	50
max_length	int, default=500	Maximum length of synthetic panel’s series.	500
n_static_features	int, default=0	Number of static exogenous variables for synthetic panel’s series.	0
equal_ends	bool, default=False	Series should end in the same date stamp ds.	False
static_as_categorical	bool, default=True	Static features should have a categorical data type.	True
with_trend	bool, default=False	Series should have a (positive) trend.	False
seed	int, default=0	Random seed used for generating the data.	0
engine	str, default=‘pandas’	Output Dataframe type.	‘pandas’

Returns:

Type	Description
DataFrame	pandas or polars DataFrame: Synthetic panel with columns [unique_id, ds, y] and exogenous features.

Generate 20 series with lengths between 100 and 1,000.

n_series = 20
min_length = 100
max_length = 1000

series = generate_daily_series(n_series, min_length, max_length)
series

	unique_id	ds	y
0	id_00	2000-01-01	0.395863
1	id_00	2000-01-02	1.264447
2	id_00	2000-01-03	2.284022
3	id_00	2000-01-04	3.462798
4	id_00	2000-01-05	4.035518
…	…	…	…
12446	id_19	2002-03-11	0.309275
12447	id_19	2002-03-12	1.189464
12448	id_19	2002-03-13	2.325032
12449	id_19	2002-03-14	3.333198
12450	id_19	2002-03-15	4.306117

We can also add static features to each serie (these can be things like product_id or store_id). Only the first static feature (static_0) is relevant to the target.

n_static_features = 2

series_with_statics = generate_daily_series(n_series, min_length, max_length, n_static_features)
series_with_statics

	unique_id	ds	y	static_0	static_1
0	id_00	2000-01-01	7.521388	18	10
1	id_00	2000-01-02	24.024502	18	10
2	id_00	2000-01-03	43.396423	18	10
3	id_00	2000-01-04	65.793168	18	10
4	id_00	2000-01-05	76.674843	18	10
…	…	…	…	…	…
12446	id_19	2002-03-11	27.834771	89	42
12447	id_19	2002-03-12	107.051746	89	42
12448	id_19	2002-03-13	209.252845	89	42
12449	id_19	2002-03-14	299.987801	89	42
12450	id_19	2002-03-15	387.550536	89	42

for i in range(n_static_features):
    assert all(series_with_statics.groupby('unique_id')[f'static_{i}'].nunique() == 1)

If equal_ends=False (the default) then every serie has a different end date.

assert series_with_statics.groupby('unique_id')['ds'].max().nunique() > 1

We can have all of them end at the same date by specifying equal_ends=True.

series_equal_ends = generate_daily_series(n_series, min_length, max_length, equal_ends=True)

assert series_equal_ends.groupby('unique_id')['ds'].max().nunique() == 1

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generate_prices_for_series

generate_prices_for_series(series, horizon=7, seed=0)

series_for_prices = generate_daily_series(20, n_static_features=2, equal_ends=True)
series_for_prices.rename(columns={'static_1': 'product_id'}, inplace=True)
prices_catalog = generate_prices_for_series(series_for_prices, horizon=7)
prices_catalog

	ds	unique_id	price
0	2000-10-05	id_00	0.548814
1	2000-10-06	id_00	0.715189
2	2000-10-07	id_00	0.602763
3	2000-10-08	id_00	0.544883
4	2000-10-09	id_00	0.423655
…	…	…	…
5009	2001-05-17	id_19	0.288027
5010	2001-05-18	id_19	0.846305
5011	2001-05-19	id_19	0.791284
5012	2001-05-20	id_19	0.578636
5013	2001-05-21	id_19	0.288589

test_eq(set(prices_catalog['unique_id']), set(series_for_prices['unique_id']))
test_fail(lambda: generate_prices_for_series(series), contains='equal ends')

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PredictionIntervals

PredictionIntervals(n_windows=2, h=1, method='conformal_distribution', scale_estimator=None)

Class for storing prediction intervals metadata information.