Metadata-Version: 2.1
Name: AutoTS
Version: 0.2.1
Summary: Automated Time Series Forecasting
Home-page: https://github.com/winedarksea/AutoTS
Author: Colin Catlin
Author-email: colin.catlin@syllepsis.live
License: MIT
Description: # AutoTS
        
        <img src="/img/autots_logo.png" width="400" height="184" title="AutoTS Logo">
        
        **Model Selection for Multiple Time Series**
        
        Simple package for comparing and predicting with open-source time series implementations.
        
        For other time series needs, check out the list [here](https://github.com/MaxBenChrist/awesome_time_series_in_python).
        
        ## Features
        * Twenty available model classes, with tens of thousands of possible hyperparameter configurations
        * Finds optimal time series models by genetic programming
        * Handles univariate and multivariate/parallel time series
        * Point and probabilistic forecasts
        * Ability to handle messy data by learning optimal NaN imputation and outlier removal
        * Ability to add external known-in-advance regressor
        * Allows automatic ensembling of best models
        * Multiple cross validation options
        * Subsetting and weighting to improve search on many multivariate series
        * Option to use one or a combination of metrics for model selection
        * Ability to upsample data to a custom frequency
        * Import and export of templates allowing greater user customization
        
        ## Basic Use
        ```
        pip install autots
        ```
        This includes dependencies for basic models, but additonal packages are required for some models and methods.
        
        Input data is expected to come in a 'long' format with three columns: 
        * Date (ideally already in pd.DateTime format)
        * Value
        * Series ID. For a single time series, series_id can be `= None`. 
        
        The column name for each of these is passed to .fit(). 
        
        ```
        
        # also: _hourly, _daily, _weekly, or _yearly
        from autots.datasets import load_monthly 
        df_long = load_monthly()
        
        from autots import AutoTS
        model = AutoTS(forecast_length=3, frequency='infer',
                       prediction_interval=0.9, ensemble=None,
        			   model_list='superfast',
                       max_generations=5, num_validations=2,
        			   validation_method='even')
        model = model.fit(df_long, date_col='datetime',
        				  value_col='value', id_col='series_id')
        
        # Print the name of the best model
        print(model)
        
        prediction = model.predict()
        # point forecasts dataframe
        forecasts_df = prediction.forecast
        # accuracy of all tried model results
        model_results = model.results()
        # and aggregated from cross validation
        validation_results = model.results("validation")
        
        ```
        
        Check out [extended_tutorial.md](https://winedarksea.github.io/AutoTS/build/html/source/tutorial.html) for a more detailed guide to features!
        
        ## How to Contribute:
        * Give feedback on where you find the documentation confusing
        * Use AutoTS and...
        	* Report errors and request features by adding Issues on GitHub
        	* Posting the top model templates for your data (to help improve the starting templates)
        	* Feel free to recommend different search grid parameters for your favorite models
        * And, of course, contributing to the codebase directly on GitHub!
        
        
        *Also known as Project CATS (Catlin's Automated Time Series) hence the logo.*
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.5
Description-Content-Type: text/markdown
Provides-Extra: additional
