datasetsforecast

Name	datasetsforecast JSON
Version	1.0.0 JSON
	download
home_page	https://github.com/Nixtla/datasetsforecast/tree/main/
Summary	Datasets for Time series forecasting
upload_time	2024-12-06 00:11:48
maintainer	None
docs_url	None
author	Nixtla
requires_python	>=3.9
license	MIT License
keywords	time-series forecasting datasets
VCS
bugtrack_url
requirements	No requirements were recorded.
Travis-CI	No Travis.
coveralls test coverage	No coveralls.

            # datasetsforecast

<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->

Datasets for time series forecasting

## Install

``` sh
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
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))

            

Raw data

            {
    "_id": null,
    "home_page": "https://github.com/Nixtla/datasetsforecast/tree/main/",
    "name": "datasetsforecast",
    "maintainer": null,
    "docs_url": null,
    "requires_python": ">=3.9",
    "maintainer_email": null,
    "keywords": "time-series forecasting datasets",
    "author": "Nixtla",
    "author_email": "business@nixtla.io",
    "download_url": "https://files.pythonhosted.org/packages/2d/77/a5a76b16666132cac1368d8da1134f5173ca20a5433dd4a685b1dd7f46fe/datasetsforecast-1.0.0.tar.gz",
    "platform": null,
    "description": "# datasetsforecast\n\n<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->\n\nDatasets for time series forecasting\n\n## Install\n\n``` sh\npip install datasetsforecast\n```\n\n## Datasets\n\n- [Favorita](https://nixtlaverse.nixtla.io/datasetsforecast/favorita.html)\n- [Hierarchical](https://nixtlaverse.nixtla.io/datasetsforecast/hierarchical.html)\n- [Long\n  horizon](https://nixtlaverse.nixtla.io/datasetsforecast/long_horizon.html)\n- [M3](https://nixtlaverse.nixtla.io/datasetsforecast/m3.html)\n- [M4](https://nixtlaverse.nixtla.io/datasetsforecast/m4.html)\n- [M5](https://nixtlaverse.nixtla.io/datasetsforecast/m5.html)\n- [PHM2008](https://nixtlaverse.nixtla.io/datasetsforecast/phm2008.html)\n\n## How to use\n\nAll the modules have a `load` method which you can use to load the\ndataset for a specific group. If you don\u2019t have the data locally it will\nbe downloaded for you.\n\n``` python\nfrom datasetsforecast.phm2008 import PHM2008\n```\n\n``` python\ntrain_df, test_df = PHM2008.load(directory='data', group='FD001')\ntrain_df.shape, test_df.shape\n```\n\n    ((20631, 17), (13096, 17))\n",
    "bugtrack_url": null,
    "license": "MIT License",
    "summary": "Datasets for Time series forecasting",
    "version": "1.0.0",
    "project_urls": {
        "Homepage": "https://github.com/Nixtla/datasetsforecast/tree/main/"
    },
    "split_keywords": [
        "time-series",
        "forecasting",
        "datasets"
    ],
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "86e23fc14cd4ed4633e8d69e81f85dc7cf4615852dd605d5ebdfbf9bccc2c2a3",
                "md5": "32785be0a5e5f3698b7835694d616197",
                "sha256": "0a958447ceaf94ff5e4aa6318d3a2f277fb6aecdf8724ac021f54bf6a7461a9d"
            },
            "downloads": -1,
            "filename": "datasetsforecast-1.0.0-py3-none-any.whl",
            "has_sig": false,
            "md5_digest": "32785be0a5e5f3698b7835694d616197",
            "packagetype": "bdist_wheel",
            "python_version": "py3",
            "requires_python": ">=3.9",
            "size": 34537,
            "upload_time": "2024-12-06T00:11:46",
            "upload_time_iso_8601": "2024-12-06T00:11:46.955107Z",
            "url": "https://files.pythonhosted.org/packages/86/e2/3fc14cd4ed4633e8d69e81f85dc7cf4615852dd605d5ebdfbf9bccc2c2a3/datasetsforecast-1.0.0-py3-none-any.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "2d77a5a76b16666132cac1368d8da1134f5173ca20a5433dd4a685b1dd7f46fe",
                "md5": "d5d0561ad2b9fde7e4e6358ed5875bc8",
                "sha256": "7daa1342fe30cef66f9f61b08cf17b0b68b4de3c02a61ab2569e71b72779a8ad"
            },
            "downloads": -1,
            "filename": "datasetsforecast-1.0.0.tar.gz",
            "has_sig": false,
            "md5_digest": "d5d0561ad2b9fde7e4e6358ed5875bc8",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": ">=3.9",
            "size": 30586,
            "upload_time": "2024-12-06T00:11:48",
            "upload_time_iso_8601": "2024-12-06T00:11:48.523424Z",
            "url": "https://files.pythonhosted.org/packages/2d/77/a5a76b16666132cac1368d8da1134f5173ca20a5433dd4a685b1dd7f46fe/datasetsforecast-1.0.0.tar.gz",
            "yanked": false,
            "yanked_reason": null
        }
    ],
    "upload_time": "2024-12-06 00:11:48",
    "github": true,
    "gitlab": false,
    "bitbucket": false,
    "codeberg": false,
    "github_user": "Nixtla",
    "github_project": "datasetsforecast",
    "travis_ci": false,
    "coveralls": false,
    "github_actions": true,
    "lcname": "datasetsforecast"
}