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        "author": "Alejandro De Barros",
        "author_email": "alejandrodbn@gmail.com",
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        "classifiers": [
            "Development Status :: 4 - Beta",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7"
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        "description": "# Forecast_x: Toolkit with Naive models for time series.\n![Forecast_x logo](https://www.alejandrodebarros.com/forecast_x.png)\n\n[![PyPI version](https://badge.fury.io/py/forecast-x.svg)](https://badge.fury.io/py/forecast-x)\n[![license](https://img.shields.io/github/license/mashape/apistatus.svg?maxAge=2592000)](https://github.com/alejandrodbn/forecast/blob/master/LICENSE)\n[![Downloads](https://pepy.tech/badge/forecast-x)](https://pepy.tech/project/forecast-x)\n[![Downloads](https://pepy.tech/badge/forecast-x/month)](https://pepy.tech/project/forecast-x)\n[![Downloads](https://pepy.tech/badge/forecast-x/week)](https://pepy.tech/project/forecast-x)\n\n**If you're interested in financially supporting my open source, consider [visiting this link](https://cash.me/$AlejandroDeBarros). \nYour support helps tremendously with sustainability this work.\n\n__Forecast_x__ is a pure python package that provides different naive models for fitting multiple time series,\nespecially in batch process, due to its powerful flexibility and easy usage.\n\nThis library can be used in several industries with focus on manufacturing processes, where forecasting models\n with low cost of error are needed to plan raw material consumption.\n\n\n## Models\n\nForecast_x uses the following models to produce forecast:\n\n- Model Naive\n- Model Seas Naive\n- Model Mean Two Periods\n- Model Mean Three Periods\n- Model Half Seas Mean\n- Model Seas Period Mean\n- Model Double Seas Mean\n- Model Seas Growth\n- Model Expo Weighted\n- Model Threefith Mean\n- Model Multi Seas Mean\n- Model Seas Double Mean Growth\n- Model Grand Mean\n- Model Smooth Grand Mean\n- Model Last Seas Mean\n- Model Current Mean Seas\n- Model Smooth Double Seas Naive\n- Model Truncated Mean\n- Model Harmonic Mean\n- Model Heronian Mean\n\n## Getting started: 10 seconds to Forecast_x\n\nHere is how-to use `Forecast_x` models:\n\n```python\nfrom forecast_x import forecast_x as fx\n\n# time series observation\ntime_series = [51, 17, 28, 37, 52, 21, 34, 47, 38, 35, 7, 27]\nfreq = 12 # monthly\nh = 12 # forecast months ahead\n\n# Creating the forecast object\nf = fx.forecast(time_series, freq, h)\n\n# Applying any the model from the package\nmodel = f.model_naive()\n\n# The model variable would produce a list of three elements:\n# - Fitting Values\n# - Error\n# - Forecast\nmodel\n\n```\nTo get only forecast of a given model you should use:\n\n```python\n\nf.get_forecast('model_naive')\n\n```\n\nTo allow the package to select the best fit based on multiple cross validation you should use:\n\n```python\n\nmodel = f.best_model()\n# forecast_x would select 'model_seas_period_mean' as best model based on test results\nmodel\n# Getting forecast from best model\nforecast = get_forecast(model)\n\n```\n\n## Installation\n\n```sh\n# or PyPI\npip install forecast_x\n```\n\n\n## Dependencies\n- None.\n\n\n## Python Version\n\nSupported on 3.5, 3.6 and 3.7.\n\n\n## License\n[MIT](LICENSE)\n\n\n## Documentation\nThe official documentation will be available soon.\n\n\n## Citation\n\nCitations or acknowledge on any work or project are very welcome:\n\n> Alejandro De Barros. 2018.\n> _Forecast_x: An open source forecasting tool for time series library for Python_\n\n## Meta\n\nAlejandro De Barros \u00e2\u20ac\u201c (https://twitter.com/alejandrodbn) \u00e2\u20ac\u201c alejandrodbn@gmail.com\n\nDistributed under the MIT license. See ``LICENSE`` for more information.\n\n[https://github.com/alejandrodbn/forecast](https://github.com/alejandrodbn/)\n\n## Code of Conduct\n\nEveryone interacting with this project's codebases, issue trackers, and mailing lists is expected to follow the Code of Conduct.\n\n\n\n\n",
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