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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,
    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:
NameTypeDescriptionDefault
n_seriesintNumber of series for synthetic panel.required
min_lengthint, default=50Minimum length of synthetic panel’s series.50
max_lengthint, default=500Maximum length of synthetic panel’s series.500
n_static_featuresint, default=0Number of static exogenous variables for synthetic panel’s series.0
equal_endsbool, default=FalseSeries should end in the same date stamp ds.False
static_as_categoricalbool, default=TrueStatic features should have a categorical data type.True
with_trendbool, default=FalseSeries should have a (positive) trend.False
seedint, default=0Random seed used for generating the data.0
enginestr, default=‘pandas’Output Dataframe type.‘pandas’
Returns:
TypeDescription
DataFramepandas 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_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, 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
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=2, h=1, method='conformal_distribution')
Class for storing prediction intervals metadata information.