Metadata-Version: 2.3
Name: datasetsforecast
Version: 1.0.1
Summary: Datasets for Time series forecasting
Keywords: time-series,forecasting,datasets
Author: Nixtla
Author-email: Nixtla <business@nixtla.io>
License: MIT License
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: Natural Language :: English
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Requires-Dist: aiohttp
Requires-Dist: numpy
Requires-Dist: scikit-learn
Requires-Dist: pandas[plot]<3.0.0
Requires-Dist: requests
Requires-Dist: tqdm
Requires-Dist: utilsforecast>=0.0.8
Requires-Dist: xlrd>=1.0.0
Requires-Python: >=3.10
Project-URL: Homepage, https://github.com/Nixtla/datasetsforecast
Project-URL: Repository, https://github.com/Nixtla/datasetsforecast
Project-URL: Documentation, https://nixtlaverse.nixtla.io/datasetsforecast/
Description-Content-Type: text/markdown

# datasetsforecast

> Datasets for time series forecasting

## Install

``` sh
pip install datasetsforecast
```

## Datasets

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

## 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
from datasetsforecast.phm2008 import PHM2008
```

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

    ((20631, 17), (13096, 17))
