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

# Hierarchical

> Hierarchical dataset

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

1. Australian Monthly Labour: [Labour](#labour),
2. SF Bay Area daily Traffic: [Traffic](#traffic), [OldTraffic](#oldtraffic),
3. Quarterly Australian Tourism Visits: ([TourismSmall](#tourismsmall)),
4. Monthly Australian Tourism visits: [TourismLarge](#tourismlarge), [OldTourismLarge](#oldtourismlarge),
5. daily Wikipedia article views: [Wiki2](#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

* [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)
* [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)
* [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)

##

### `Labour`

```python theme={null}
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`

```python theme={null}
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`

```python theme={null}
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`

```python theme={null}
Traffic(freq='D', horizon=14, papers_horizon=7, seasonality=7, test_size=91, tags_names=('Level1', 'Level2', 'Level3', 'Level4'))
```

### `Wiki2`

```python theme={null}
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`

```python theme={null}
OldTraffic(freq='D', horizon=1, papers_horizon=1, seasonality=7, test_size=91, tags_names=('Level1', 'Level2', 'Level3', 'Level4'))
```

### `HierarchicalData`

#### `HierarchicalData.download`

```python theme={null}
download(directory)
```

Download Hierarchical Datasets.

**Parameters:**

| Name        | Type                     | Description                         | Default    |
| ----------- | ------------------------ | ----------------------------------- | ---------- |
| `directory` | <code>[str](#str)</code> | Directory path to download dataset. | *required* |

#### `HierarchicalData.load`

```python theme={null}
load(directory, group, cache=True)
```

Downloads hierarchical forecasting benchmark datasets.

**Parameters:**

| Name        | Type                       | Description                              | Default           |
| ----------- | -------------------------- | ---------------------------------------- | ----------------- |
| `directory` | <code>[str](#str)</code>   | Directory where data will be downloaded. | *required*        |
| `group`     | <code>[str](#str)</code>   | Group name.                              | *required*        |
| `cache`     | <code>[bool](#bool)</code> | If `True` saves and loads                | <code>True</code> |

**Returns:**

| Type                                                                                                 | Description                                                                                                                                                                   |
| ---------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| <code>[Tuple](#typing.Tuple)\[[DataFrame](#pandas.DataFrame), [DataFrame](#pandas.DataFrame)]</code> | 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). |