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Modeling interval trendlines: Symbolic singular spectrum analysis for interval time series
Author(s) -
Carvalho Miguel,
Martos Gabriel
Publication year - 2022
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.2801
Subject(s) - singular spectrum analysis , symbolic dynamics , interval (graph theory) , series (stratigraphy) , symbolic data analysis , computer science , time series , mathematics , extension (predicate logic) , algorithm , singular value decomposition , machine learning , theoretical computer science , paleontology , combinatorics , pure mathematics , biology , programming language
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.