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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
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.