Metadata-Version: 2.1
Name: tbats
Version: 1.0.9
Summary: BATS and TBATS for time series forecasting
Home-page: https://github.com/intive-DataScience/tbats
Author: Grzegorz Skorupa (intive)
Author-email: grzegorz.skorupa@intive.com
License: MIT License
Description: # BATS and TBATS time series forecasting
        
        Package provides BATS and TBATS time series forecasting methods described in:
        
        > De Livera, A.M., Hyndman, R.J., & Snyder, R. D. (2011), Forecasting time series with complex seasonal patterns using exponential smoothing, Journal of the American Statistical Association, 106(496), 1513-1527.
        
        
        ## Installation
        
        From pypi:
        
        ```bash
        pip install tbats
        ```
        
        Import via:
        
        ```python
        from tbats import BATS, TBATS
        ```
        
        ## Minimal working example:
        
        ```python
        from tbats import TBATS
        import numpy as np
        
        # required on windows for multi-processing,
        # see https://docs.python.org/2/library/multiprocessing.html#windows
        if __name__ == '__main__':
            np.random.seed(2342)
            t = np.array(range(0, 160))
            y = 5 * np.sin(t * 2 * np.pi / 7) + 2 * np.cos(t * 2 * np.pi / 30.5) + \
                ((t / 20) ** 1.5 + np.random.normal(size=160) * t / 50) + 10
            
            # Create estimator
            estimator = TBATS(seasonal_periods=[14, 30.5])
            
            # Fit model
            fitted_model = estimator.fit(y)
            
            # Forecast 14 steps ahead
            y_forecasted = fitted_model.forecast(steps=14)
            
            # Summarize fitted model
            print(fitted_model.summary())
        ```
        
        Reading model details
        
        ```python
        # Time series analysis
        print(fitted_model.y_hat) # in sample prediction
        print(fitted_model.resid) # in sample residuals
        print(fitted_model.aic)
        
        # Reading model parameters
        print(fitted_model.params.alpha)
        print(fitted_model.params.beta)
        print(fitted_model.params.x0)
        print(fitted_model.params.components.use_box_cox)
        print(fitted_model.params.components.seasonal_harmonics)
        ```
        
        See **examples** directory for more details
        
        ## For Contributors
        
        Building package:
        
        ```bash
        pip install -e .[dev]
        ```
        
        Unit and integration tests:
        
        ```bash
        python setup.py test
        ```
        
        R forecast package comparison tests. Those DO NOT RUN with default test command, you need R forecast package installed:
        ```bash
        python setup.py test_r
        ```
        
        ## Comparison to R implementation
        
        Python implementation is meant to be as much as possible equivalent to R implementation in forecast package.
        
        - BATS in R https://www.rdocumentation.org/packages/forecast/versions/8.4/topics/bats
        - TBATS in R: https://www.rdocumentation.org/packages/forecast/versions/8.4/topics/tbats
        
        
        
        
        
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
Provides-Extra: dev
