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

TL;DR

This paper extends singular spectrum analysis to interval-valued time series, enabling decomposition and forecasting of complex stochastic processes with applications to financial market analysis.

Contribution

It introduces a novel extension of SSA for interval data, including multivariate generalizations, and demonstrates its effectiveness through simulations and real-world financial case studies.

Findings

Effective decomposition of interval time series into trend, cycle, noise

Accurate forecasting of interval-valued processes using linear recurrent methods

Successful application to Argentina Stock Market during turbulent periods

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.