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STR: Seasonal-Trend Decomposition Using Regression

Author

Listed:
  • Alexander Dokumentov

    (Let’s Forecast, Parkdale, Victoria 3195, Australia)

  • Rob J. Hyndman

    (Department of Econometrics & Business Statistics, Monash University, Clayton, Victoria 3800, Australia)

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.

Suggested Citation

  • Alexander Dokumentov & Rob J. Hyndman, 2022. "STR: Seasonal-Trend Decomposition Using Regression," INFORMS Joural on Data Science, INFORMS, vol. 1(1), pages 50-62, April.
    • Handle: RePEc:inm:orijds:v:1:y:2022:i:1:p:50-62
    DOI: 10.1287/ijds.2021.0004
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    References listed on IDEAS

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    Cited by:

    1. Jiang, Haiyang & Du, Ershun & He, Boyu & Zhang, Ning & Wang, Peng & Li, Fuqiang & Ji, Jie, 2023. "Analysis and modeling of seasonal characteristics of renewable energy generation," Renewable Energy, Elsevier, vol. 219(P1).

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