Local
Utils
Local
Utils
from fastcore.test import test_eq, test_fail
from nbdev import show_doc
source
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.