> ## 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.

# datasetsforecast

Datasets for time series forecasting

## Install

```sh theme={null}
pip install datasetsforecast
```

## Datasets

* [Favorita](https://nixtlaverse.nixtla.io/datasetsforecast/favorita.html)
* [Hierarchical](https://nixtlaverse.nixtla.io/datasetsforecast/hierarchical.html)
* [Long
  horizon](https://nixtlaverse.nixtla.io/datasetsforecast/long_horizon.html)
* [M3](https://nixtlaverse.nixtla.io/datasetsforecast/m3.html)
* [M4](https://nixtlaverse.nixtla.io/datasetsforecast/m4.html)
* [M5](https://nixtlaverse.nixtla.io/datasetsforecast/m5.html)
* [PHM2008](https://nixtlaverse.nixtla.io/datasetsforecast/phm2008.html)

## How to use

All the modules have a `load` method which you can use to load the
dataset for a specific group. If you don’t have the data locally it will
be downloaded for you.

```python theme={null}
from datasetsforecast.phm2008 import PHM2008
```

```python theme={null}
train_df, test_df = PHM2008.load(directory='data', group='FD001')
train_df.shape, test_df.shape
```

```text theme={null}
((20631, 17), (13096, 17))
```