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.

TypeDefaultDetails
n_seriesintNumber of series for synthetic panel.
min_lengthint50Minimum length of synthetic panel’s series.
max_lengthint500Maximum length of synthetic panel’s series.
n_static_featuresint0Number of static exogenous variables for synthetic panel’s series.
equal_endsboolFalseSeries should end in the same date stamp ds.
static_as_categoricalboolTrueStatic features should have a categorical data type.
with_trendboolFalseSeries should have a (positive) trend.
seedint0Random seed used for generating the data.
enginestrpandasOutput Dataframe type.
ReturnsUnionSynthetic 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_iddsy
0id_002000-01-010.395863
1id_002000-01-021.264447
2id_002000-01-032.284022
3id_002000-01-043.462798
4id_002000-01-054.035518
…………
12446id_192002-03-110.309275
12447id_192002-03-121.189464
12448id_192002-03-132.325032
12449id_192002-03-143.333198
12450id_192002-03-154.306117

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_iddsystatic_0static_1
0id_002000-01-017.5213881810
1id_002000-01-0224.0245021810
2id_002000-01-0343.3964231810
3id_002000-01-0465.7931681810
4id_002000-01-0576.6748431810
………………
12446id_192002-03-1127.8347718942
12447id_192002-03-12107.0517468942
12448id_192002-03-13209.2528458942
12449id_192002-03-14299.9878018942
12450id_192002-03-15387.5505368942
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
dsunique_idprice
02000-10-05id_000.548814
12000-10-06id_000.715189
22000-10-07id_000.602763
32000-10-08id_000.544883
42000-10-09id_000.423655
…………
50092001-05-17id_190.288027
50102001-05-18id_190.846305
50112001-05-19id_190.791284
50122001-05-20id_190.578636
50132001-05-21id_190.288589
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.

OptimizationCore