Modeling Interval Trendlines: Symbolic Singular Spectrum Analysis for Interval Time Series
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.
Cite
@article{arxiv.2011.03872,
title = {Modeling Interval Trendlines: Symbolic Singular Spectrum Analysis for Interval Time Series},
author = {Miguel de Carvalho and Gabriel Martos},
journal= {arXiv preprint arXiv:2011.03872},
year = {2020}
}