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Multiple seasonal STL decomposition with discrete-interval moving seasonalities
Applied Mathematics and Computation ( IF 4 ) Pub Date : 2022-07-18 , DOI: 10.1016/j.amc.2022.127398
Oscar Trull, J. Carlos García-Díaz, A. Peiró-Signes

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.



中文翻译:

具有离散间隔移动季节性的多季节 STL 分解

将时间序列分解为组件是理解序列行为的一个非常有用的工具。分解使得通过趋势成分和季节性成分区分长期和短期行为成为可能。在分解方法中,STL(基于黄土的季节性趋势分解)方法以其通用性和稳健性而著称。然而,这种方法有一个主要缺点:它适用于单一季节性,并且不处理日历效应。在本文中,我们提出了一种基于 STL 的新分解方法,该方法允许使用不同的季节性,同时允许使用离散间隔移动季节性 (MSTL-DIMS) 将日历效应和特殊事件引入模型。为了显示所获得的改进,

更新日期:2022-07-19
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