Forecasting stochastic processes using singular spectrum analysis: Aspects of the theory and application

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• 作者：M. Atikur Rahman Khan^a ; ^{Atikur.Khan@csiro.au}Author Vitae ; D.S. Poskitt^b ; ^{Donald.Poskitt@monash.edu}Author Vitae
• 关键词：Linear recurrent formula ; Mean squared forecast error ; Signal dimension ; Window length
• 刊名：International Journal of Forecasting
• 出版年：2017
• 出版时间：January-March 2017
• 年：2017
• 卷：33
• 期：1
• 页码：199-213
• 全文大小：649 K
• 卷排序：33

文摘

This paper presents theoretical results on the properties of forecasts obtained by using singular spectrum analysis to forecast time series that are realizations of stochastic processes. The mean squared forecast errors are derived under broad regularity conditions, and it is shown that, in practice, the forecasts obtained will converge to their population ensemble counterparts. The theoretical results are illustrated by examining the performances of singular spectrum analysis forecasts when applied to autoregressive processes and a random walk process. Simulation experiments suggest that the asymptotic properties developed are reflected in the behaviour of observed finite samples. Empirical applications using real world data sets indicate that forecasts based on singular spectrum analysis are competitive with other methods currently in vogue.