STR: Seasonal-Trend Decomposition Using Regression

Alexander Dokumentov () and Rob Hyndman
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Alexander Dokumentov: Let’s Forecast, Parkdale, Victoria 3195, Australia

INFORMS Joural on Data Science, 2022, vol. 1, issue 1, 50-62

Abstract: We propose a new method for decomposing seasonal data: a seasonal-trend decomposition using regression (STR). Unlike other decomposition methods, STR allows for multiple seasonal and cyclic components, covariates, seasonal patterns that may have noninteger periods, and seasonality with complex topology. It can be used for time series with any regular time index, including hourly, daily, weekly, monthly, or quarterly data. It is competitive with existing methods when they exist and tackles many more decomposition problems than other methods allow. STR is based on a regularized optimization and so is somewhat related to ridge regression. Because it is based on a statistical model, we can easily compute confidence intervals for components, something that is not possible with most existing decomposition methods (such as seasonal-trend decomposition using Loess, X-12-ARIMA, SEATS-TRAMO, etc.). Our model is implemented in the R package stR , so it can be applied by anyone to their own data.

Keywords: time series decomposition; seasonal data; Tikhonov regularization; ridge regression; LASSO; STL; TBATS; X-12-ARIMA; BSM (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:inm:orijds:v:1:y:2022:i:1:p:50-62

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