Two-dimensional cellular automata and the analysis of correlated time series

TL;DR

This paper introduces a novel two-dimensional cellular automaton model to analyze correlated time series, enabling improved gap filling and prediction in rainfall data compared to traditional methods.

Contribution

The paper presents a new cellular automaton-based approach for modeling and analyzing correlated time series as a unified system.

Findings

Outperforms Kalman smoothing in rainfall data prediction

Effectively fills gaps in correlated time series

Provides a new framework for time series analysis

Abstract

Correlated time series are time series that, by virtue of the underlying process to which they refer, are expected to influence each other strongly. We introduce a novel approach to handle such time series, one that models their interaction as a two-dimensional cellular automaton and therefore allows them to be treated as a single entity. We apply our approach to the problems of filling gaps and predicting values in rainfall time series. Computational results show that the new approach compares favorably to Kalman smoothing and filtering.