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        "author": "Grzegorz Skorupa (intive)",
        "author_email": "grzegorz.skorupa@intive.com",
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        "description": "# BATS and TBATS time series forecasting\n\nPackage provides BATS and TBATS time series forecasting methods described in:\n\n> 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.\n\n\n## Installation\n\nFrom pypi:\n\n```bash\npip install tbats\n```\n\nImport via:\n\n```python\nfrom tbats import BATS, TBATS\n```\n\n## Minimal working example:\n\n```python\nfrom tbats import TBATS\nimport numpy as np\n\n# required on windows for multi-processing,\n# see https://docs.python.org/2/library/multiprocessing.html#windows\nif __name__ == '__main__':\n    np.random.seed(2342)\n    t = np.array(range(0, 160))\n    y = 5 * np.sin(t * 2 * np.pi / 7) + 2 * np.cos(t * 2 * np.pi / 30.5) + \\\n        ((t / 20) ** 1.5 + np.random.normal(size=160) * t / 50) + 10\n\n    # Create estimator\n    estimator = TBATS(seasonal_periods=[14, 30.5])\n\n    # Fit model\n    fitted_model = estimator.fit(y)\n\n    # Forecast 14 steps ahead\n    y_forecasted = fitted_model.forecast(steps=14)\n\n    # Summarize fitted model\n    print(fitted_model.summary())\n```\n\nReading model details\n\n```python\n# Time series analysis\nprint(fitted_model.y_hat) # in sample prediction\nprint(fitted_model.resid) # in sample residuals\nprint(fitted_model.aic)\n\n# Reading model parameters\nprint(fitted_model.params.alpha)\nprint(fitted_model.params.beta)\nprint(fitted_model.params.x0)\nprint(fitted_model.params.components.use_box_cox)\nprint(fitted_model.params.components.seasonal_harmonics)\n```\n\nSee **examples** directory for more details\n\n## For Contributors\n\nBuilding package:\n\n```bash\npip install -e .[dev]\n```\n\nUnit and integration tests:\n\n```bash\npython setup.py test\n```\n\nR forecast package comparison tests. Those DO NOT RUN with default test command, you need R forecast package installed:\n```bash\npython setup.py test_r\n```\n\n## Comparison to R implementation\n\nPython implementation is meant to be as much as possible equivalent to R implementation in forecast package.\n\n- BATS in R https://www.rdocumentation.org/packages/forecast/versions/8.4/topics/bats\n- TBATS in R: https://www.rdocumentation.org/packages/forecast/versions/8.4/topics/tbats\n\n\n\n\n\n\n\n\n",
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