> ## Documentation Index
> Fetch the complete documentation index at: https://nixtla-old-docs.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Hierarchical Datasets

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`](https://Nixtla.github.io/datasetsforecast/hierarchical.html#labour)),
SF Bay Area daily Traffic
([`Traffic`](https://Nixtla.github.io/datasetsforecast/hierarchical.html#traffic),
[`OldTraffic`](https://Nixtla.github.io/datasetsforecast/hierarchical.html#oldtraffic)),
Quarterly Australian Tourism Visits
([`TourismSmall`](https://Nixtla.github.io/datasetsforecast/hierarchical.html#tourismsmall)),
Monthly Australian Tourism visits
([`TourismLarge`](https://Nixtla.github.io/datasetsforecast/hierarchical.html#tourismlarge),
[`OldTourismLarge`](https://Nixtla.github.io/datasetsforecast/hierarchical.html#oldtourismlarge)),
and daily Wikipedia article views
([`Wiki2`](https://Nixtla.github.io/datasetsforecast/hierarchical.html#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<br />

* [Syama Sundar Rangapuram, Lucien D Werner, Konstantinos Benidis,
  Pedro Mercado, Jan Gasthaus, Tim Januschowski. (2021). “End-to-End
  Learning of Coherent Probabilistic Forecasts for Hierarchical Time
  Series”. Proceedings of the 38th International Conference on Machine
  Learning
  (ICML).](https://proceedings.mlr.press/v139/rangapuram21a.html)<br />
* [Kin G. Olivares, O. Nganba Meetei, Ruijun Ma, Rohan Reddy, Mengfei
  Cao, Lee Dicker (2022).”Probabilistic Hierarchical Forecasting with
  Deep Poisson Mixtures”. International Journal Forecasting, special
  issue.](https://doi.org/10.1016/j.ijforecast.2023.04.007)<br />
* [Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodriguez, Chao
  Zhang, and B. Prakash. PROFHIT: Probabilistic robust forecasting for
  hierarchical time-series. Computing Research Repository.URL
  https://arxiv.org/abs/2206.07940.](https://arxiv.org/abs/2206.07940)<br />

***

<a href="https://github.com/Nixtla/datasetsforecast/blob/main/datasetsforecast/hierarchical.py#L19" target="_blank" style={{ float: "right", fontSize: "smaller" }}>source</a>

### Labour

> ```text theme={null}
>  Labour (freq:str='MS', horizon:int=8, papers_horizon:int=12,
>          seasonality:int=12, test_size:int=125,
>          tags_names:Tuple[str]=('Country', 'Country/Region',
>          'Country/Gender/Region', 'Country/Employment/Gender/Region'))
> ```

***

<a href="https://github.com/Nixtla/datasetsforecast/blob/main/datasetsforecast/hierarchical.py#L34" target="_blank" style={{ float: "right", fontSize: "smaller" }}>source</a>

### TourismLarge

> ```text theme={null}
>  TourismLarge (freq:str='MS', horizon:int=12, papers_horizon:int=12,
>                seasonality:int=12, test_size:int=57,
>                tags_names:Tuple[str]=('Country', 'Country/State',
>                'Country/State/Zone', 'Country/State/Zone/Region',
>                'Country/Purpose', 'Country/State/Purpose',
>                'Country/State/Zone/Purpose',
>                'Country/State/Zone/Region/Purpose'))
> ```

***

<a href="https://github.com/Nixtla/datasetsforecast/blob/main/datasetsforecast/hierarchical.py#L53" target="_blank" style={{ float: "right", fontSize: "smaller" }}>source</a>

### TourismSmall

> ```text theme={null}
>  TourismSmall (freq:str='Q', horizon:int=4, papers_horizon:int=4,
>                seasonality:int=4, test_size:int=9,
>                tags_names:Tuple[str]=('Country', 'Country/Purpose',
>                'Country/Purpose/State',
>                'Country/Purpose/State/CityNonCity'))
> ```

***

<a href="https://github.com/Nixtla/datasetsforecast/blob/main/datasetsforecast/hierarchical.py#L68" target="_blank" style={{ float: "right", fontSize: "smaller" }}>source</a>

### Traffic

> ```text theme={null}
>  Traffic (freq:str='D', horizon:int=14, papers_horizon:int=7,
>           seasonality:int=7, test_size:int=91,
>           tags_names:Tuple[str]=('Level1', 'Level2', 'Level3', 'Level4'))
> ```

***

<a href="https://github.com/Nixtla/datasetsforecast/blob/main/datasetsforecast/hierarchical.py#L83" target="_blank" style={{ float: "right", fontSize: "smaller" }}>source</a>

### Wiki2

> ```text theme={null}
>  Wiki2 (freq:str='D', horizon:int=14, papers_horizon:int=7,
>         seasonality:int=7, test_size:int=91,
>         tags_names:Tuple[str]=('Views', 'Views/Country',
>         'Views/Country/Access', 'Views/Country/Access/Agent',
>         'Views/Country/Access/Agent/Topic'))
> ```

***

<a href="https://github.com/Nixtla/datasetsforecast/blob/main/datasetsforecast/hierarchical.py#L99" target="_blank" style={{ float: "right", fontSize: "smaller" }}>source</a>

### OldTraffic

> ```text theme={null}
>  OldTraffic (freq:str='D', horizon:int=1, papers_horizon:int=1,
>              seasonality:int=7, test_size:int=91,
>              tags_names:Tuple[str]=('Level1', 'Level2', 'Level3',
>              'Level4'))
> ```

***

<a href="https://github.com/Nixtla/datasetsforecast/blob/main/datasetsforecast/hierarchical.py#L114" target="_blank" style={{ float: "right", fontSize: "smaller" }}>source</a>

### OldTourismLarge

> ```text theme={null}
>  OldTourismLarge (freq:str='MS', horizon:int=12, papers_horizon:int=12,
>                   seasonality:int=12, test_size:int=57,
>                   tags_names:Tuple[str]=('Country', 'Country/State',
>                   'Country/State/Zone', 'Country/State/Zone/Region',
>                   'Country/Purpose', 'Country/State/Purpose',
>                   'Country/State/Zone/Purpose',
>                   'Country/State/Zone/Region/Purpose'))
> ```

***

<a href="https://github.com/Nixtla/datasetsforecast/blob/main/datasetsforecast/hierarchical.py#L142" target="_blank" style={{ float: "right", fontSize: "smaller" }}>source</a>

### HierarchicalData

> ```text theme={null}
>  HierarchicalData ()
> ```

*Initialize self. See help(type(self)) for accurate signature.*