Modeling Interval Trendlines: Symbolic Singular Spectrum Analysis for Interval Time Series

Miguel de Carvalho, Gabriel Martos

Research output: Contribution to journalArticlepeer-review

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 that covers the most recent period of turbulence that led to discussions of the government of Argentina with the International Monetary Fund.
Original languageEnglish
Number of pages22
JournalJournal of Forecasting
Publication statusAccepted/In press - 16 Jun 2021

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