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arXiv · 2011.03872

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

Miguel de Carvalho·Gabriel Martos
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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.

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BibTeXRIS

Miguel de Carvalho, Gabriel Martos. 2020-11-08. Modeling Interval Trendlines: Symbolic Singular Spectrum Analysis for Interval Time Series. https://arxiv.org/abs/2011.03872

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