{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/3"}],"enrichment":{"capability":"Implements BATS and TBATS exponential smoothing methods for forecasting time series with complex seasonal patterns, built on numpy, scipy, scikit-learn, and pmdarima.","skillfed_tags":["time-series-forecasting","seasonal-decomposition"],"use_cases":["Forecast retail sales or demand with weekly and yearly seasonal cycles without manual model specification.","Analyze electricity load or utility consumption with multiple overlapping seasonal patterns.","Predict web traffic or system metrics that exhibit both daily and weekly seasonality.","Generate point forecasts and residual diagnostics for time series with complex, non-standard seasonal structures.","Prototype time series forecasting pipelines where automatic model selection is preferred over manual tuning."],"what_it_does":"TBATS implements Bayesian Automatic Time Series forecasting methods designed to handle time series with multiple seasonal patterns and trend components. It combines exponential smoothing with automatic model selection, testing various configurations (including Box-Cox transformation and ARMA error modeling) to find the best fit. The package wraps numpy, scipy, scikit-learn, and pmdarima to perform this model search and fitting.\n\nYou provide a time series and optionally specify seasonal periods; TBATS fits a model, then forecasts forward. It exposes fitted values, residuals, model parameters, and summary statistics. The package is a Python port of the R forecast package's BATS/TBATS implementation. Fitting can be computationally intensive on long series, and the documentation includes troubleshooting guidance for slow convergence and multiprocessing issues.","worth_installing":"Yes, if you need BATS/TBATS forecasting and can tolerate an abandoned package. The library is stable, has low install friction, carries no known vulnerabilities, and is permissively licensed. However, the last release was 2023-04-17 with no active maintenance. Use it for prototyping or production work where you can accept no further updates; avoid it if you need ongoing support or compatibility with future dependency versions."},"id":"tbats","links":{"html":"https://skillfed.io/packages/tbats","md":"https://skillfed.io/packages/tbats.md","pypi":"https://pypi.org/project/tbats/"},"maintenance":{"status":"abandoned"},"meta":{"latest_release":"2023-04-17","license_spdx":null,"license_treatment":"permissive","name":"tbats","python_support":"unspecified","summary":"BATS and TBATS for time series forecasting"},"popularity":{"monthly_downloads":710553,"position":5259,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.1.3"}