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Multiple seasonal STL decomposition with discrete-interval moving seasonalities

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  • Trull, Oscar
  • García-Díaz, J. Carlos
  • Peiró-Signes, A.

Abstract

The decomposition of a time series into components is an exceptionally useful tool for understanding the behaviour of the series. The decomposition makes it possible to distinguish the long-term and the short-term behaviour through the trend component and the seasonality component. Among the decomposition methods, the STL (Seasonal Trend decomposition based on Loess) method stands out for its versatility and robustness. This method, however, has one main drawback: it works with a single seasonality, and does not deal with the calendar effect. In this article we present a new decomposition method, based on the STL, which allows the use of different seasonalities while allowing the calendar effect and special events to be introduced into the model using discrete-interval moving seasonalities (MSTL-DIMS). To show the improvements obtained, the MSTL-DIMS technique is applied to short-term load forecasting in some electricity systems, and the results are discussed.

Suggested Citation

  • Trull, Oscar & García-Díaz, J. Carlos & Peiró-Signes, A., 2022. "Multiple seasonal STL decomposition with discrete-interval moving seasonalities," Applied Mathematics and Computation, Elsevier, vol. 433(C).
    • Handle: RePEc:eee:apmaco:v:433:y:2022:i:c:s0096300322004726
    DOI: 10.1016/j.amc.2022.127398
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    References listed on IDEAS

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    1. Trull, Oscar & García-Díaz, J. Carlos & Troncoso, Alicia, 2021. "One-day-ahead electricity demand forecasting in holidays using discrete-interval moving seasonalities," Energy, Elsevier, vol. 231(C).
    2. William S. Cleveland & Douglas M. Dunn & Irma J. Terpenning, 1978. "SABL: A Resistant Seasonal Adjustment Procedure With Graphical Methods for Interpretation and Diagnosis," NBER Chapters, in: Seasonal Analysis of Economic Time Series, pages 201-241, National Bureau of Economic Research, Inc.
    3. Yin, Yi & Shang, Pengjian & Xia, Jianan, 2015. "Compositional segmentation of time series in the financial markets," Applied Mathematics and Computation, Elsevier, vol. 268(C), pages 399-412.
    4. Rafal Weron, 2006. "Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach," HSC Books, Hugo Steinhaus Center, Wroclaw University of Technology, number hsbook0601.
    5. Óscar Trull & J. Carlos García-Díaz & Alicia Troncoso, 2019. "Application of Discrete-Interval Moving Seasonalities to Spanish Electricity Demand Forecasting during Easter," Energies, MDPI, vol. 12(6), pages 1-16, March.
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    Cited by:

    1. Yan Hong & Ding Wang & Jingming Su & Maowei Ren & Wanqiu Xu & Yuhao Wei & Zhen Yang, 2023. "Short-Term Power Load Forecasting in Three Stages Based on CEEMDAN-TGA Model," Sustainability, MDPI, vol. 15(14), pages 1-28, July.

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