Modeling interval trendlines: Symbolic singular spectrum analysis for interval time series

Miguel de Carvalho and Gabriel Martos

Journal of Forecasting, 2022, vol. 41, issue 1, 167-180

Abstract: In this article we propose an extension of singular spectrum analysis for interval‐valued time series. The proposed methods can be used to decompose and forecast the dynamics governing a set‐valued stochastic process. The resulting components on which the interval time series is decomposed can be understood as interval trendlines, cycles, or noise. Forecasting can be conducted through a linear recurrent method, and we devised generalizations of the decomposition method for the multivariate setting. The performance of the proposed methods is showcased in a simulation study. We apply the proposed methods so to track the dynamics governing the Argentina Stock Market (MERVAL) in real time, in a case study over a period of turbulence that led to discussions of the government of Argentina with the International Monetary Fund.

Date: 2022
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https://doi.org/10.1002/for.2801

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Persistent link: https://EconPapers.repec.org/RePEc:wly:jforec:v:41:y:2022:i:1:p:167-180

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