Hierarchical - Nixtla

Here we host a collection of datasets used in previous hierarchical research by Rangapuram et al. [2021], Olivares et al. [2023], and Kamarthi et al. [2022]. The benchmark datasets utilized include

Australian Monthly Labour: Labour,
SF Bay Area daily Traffic: Traffic, OldTraffic,
Quarterly Australian Tourism Visits: (TourismSmall),
Monthly Australian Tourism visits: TourismLarge, OldTourismLarge,
daily Wikipedia article views: Wiki2.

Old datasets favor the original datasets with minimal target variable preprocessing (Rangapuram et al. [2021], Olivares et al. [2023]), while the remaining datasets follow PROFHIT experimental settings.

References

`Labour`

Labour(freq='MS', horizon=8, papers_horizon=12, seasonality=12, test_size=125, tags_names=('Country', 'Country/Region', 'Country/Gender/Region', 'Country/Employment/Gender/Region'))

`TourismLarge`

TourismLarge(freq='MS', horizon=12, papers_horizon=12, seasonality=12, test_size=57, tags_names=('Country', 'Country/State', 'Country/State/Zone', 'Country/State/Zone/Region', 'Country/Purpose', 'Country/State/Purpose', 'Country/State/Zone/Purpose', 'Country/State/Zone/Region/Purpose'))

`TourismSmall`

TourismSmall(freq='Q', horizon=4, papers_horizon=4, seasonality=4, test_size=9, tags_names=('Country', 'Country/Purpose', 'Country/Purpose/State', 'Country/Purpose/State/CityNonCity'))

`Traffic`

Traffic(freq='D', horizon=14, papers_horizon=7, seasonality=7, test_size=91, tags_names=('Level1', 'Level2', 'Level3', 'Level4'))

`Wiki2`

Wiki2(freq='D', horizon=14, papers_horizon=7, seasonality=7, test_size=91, tags_names=('Views', 'Views/Country', 'Views/Country/Access', 'Views/Country/Access/Agent', 'Views/Country/Access/Agent/Topic'))

`OldTraffic`

OldTraffic(freq='D', horizon=1, papers_horizon=1, seasonality=7, test_size=91, tags_names=('Level1', 'Level2', 'Level3', 'Level4'))

`HierarchicalData`

`HierarchicalData.download`

download(directory)

Download Hierarchical Datasets. Parameters:

Name	Type	Description	Default
`directory`	`str`	Directory path to download dataset.	required

`HierarchicalData.load`

load(directory, group, cache=True)

Downloads hierarchical forecasting benchmark datasets. Parameters:

Name	Type	Description	Default
`directory`	`str`	Directory where data will be downloaded.	required
`group`	`str`	Group name.	required
`cache`	`bool`	If `True` saves and loads	`True`

Returns:

Type	Description
`Tuple[DataFrame, DataFrame]`	Tuple[pd.DataFrame, pd.DataFrame]: Target time series with columns [‘unique_id’, ‘ds’, ‘y’]. Containes the base time series, Summing matrix of size (hierarchies, bottom).

​References

​

​Labour

​TourismLarge

​TourismSmall

​Traffic

​Wiki2

​OldTraffic

​HierarchicalData

​HierarchicalData.download

​HierarchicalData.load