Decompose and Conquer: Time Series Forecasting with Multiseasonal Trend Decomposition Using Loess

Amirhossein Sohrabbeig, Omid Ardakanian and Petr Musilek ()
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Amirhossein Sohrabbeig: Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada
Omid Ardakanian: Computing Science, University of Alberta, Edmonton, AB T6G 1H9, Canada
Petr Musilek: Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada

Forecasting, 2023, vol. 5, issue 4, 1-13

Abstract: Over the past few years, there has been growing attention to the Long-Term Time Series Forecasting task and solving its inherent challenges like the non-stationarity of the underlying distribution. Notably, most successful models in this area use decomposition during preprocessing. Yet, much of the recent research has focused on intricate forecasting techniques, often overlooking the critical role of decomposition, which we believe can significantly enhance the performance. Another overlooked aspect is the presence of multiseasonal components in many time series datasets. This study introduced a novel forecasting model that prioritizes multiseasonal trend decomposition, followed by a simple, yet effective forecasting approach. We submit that the right decomposition is paramount. The experimental results from both real-world and synthetic data underscore the efficacy of the proposed model, Decompose&Conquer, for all benchmarks with a great margin, around a 30–50% improvement in the error.

Keywords: time series forecasting; decomposition; multiseasonal (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2023
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