2022/04/21 by Grzegorz Dudek, Dudek, Grzegorz
Computer Science · Decision Sciences · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Methodology (stat.ME) #Neural Networks and Applications #Stock Market Forecasting Methods #Time Series Analysis and Forecasting
paper · pdf · doi:10.48550/arxiv.2204.10398
openalex publication_date 2022/04/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The decomposition of a time series is an essential task that helps to understand its very nature. It facilitates the analysis and forecasting of complex time series expressing various hidden components such as the trend, seasonal components, cyclic components and irregular fluctuations. Therefore, it is crucial in many fields for forecasting and decision processes. In recent years, many methods of time series decomposition have been developed, which extract and reveal different time series properties. Unfortunately, they neglect a very important property, i.e. time series variance. To deal with heteroscedasticity in time series, the method proposed in this work -- a seasonal-trend-dispersion decomposition (STD) -- extracts the trend, seasonal component and component related to the dispersion of the time series. We define STD decomposition in two ways: with and without an irregular component. We show how STD can be used for time series analysis and forecasting.