Utils

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

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generate_daily_series

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

Generate Synthetic Panel Series.

	Type	Default	Details
n_series	int		Number of series for synthetic panel.
min_length	int	50	Minimum length of synthetic panel’s series.
max_length	int	500	Maximum length of synthetic panel’s series.
n_static_features	int	0	Number of static exogenous variables for synthetic panel’s series.
equal_ends	bool	False	Series should end in the same date stamp ds.
static_as_categorical	bool	True	Static features should have a categorical data type.
with_trend	bool	False	Series should have a (positive) trend.
seed	int	0	Random seed used for generating the data.
engine	str	pandas	Output Dataframe type.
Returns	Union		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:pandas.core.frame.DataFrame,
                             horizon:int=7, seed:int=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:int=2, h:int=1,
                      method:str='conformal_distribution')

Class for storing prediction intervals metadata information.