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

de Carvalho, Miguel; Martos, Gabriel

doi:10.1002/for.2801

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

Journal Article · 19 July 2021 · Journal of Forecasting

DOI: https://doi.org/10.1002/for.2801 · OSTI ID:1808888

^[1]; Martos, Gabriel ^[2]

School of Mathematics University of Edinburgh Edinburgh UK
Department of Mathematics and Statistics Universidad Torcuato Di Tella Buenos Aires Argentina

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.

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Sponsoring Organization:: USDOE

OSTI ID:: 1808888

Journal Information:: Journal of Forecasting, Journal Name: Journal of Forecasting Journal Issue: 1 Vol. 41; ISSN 0277-6693

Publisher:: Wiley Blackwell (John Wiley & Sons)Copyright Statement

Country of Publication:: United Kingdom

Language:: English

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