Multilayer Horizontal Visibility Graphs for Multivariate Time Series Analysis

TL;DR

This paper introduces a novel multilayer horizontal visibility graph method for analyzing multivariate time series, capturing cross-variable dependencies more effectively than previous approaches, with extensive validation on synthetic and real data.

Contribution

It proposes a new cross-horizontal visibility mapping and multilayer graph framework, extending topological measures for multivariate time series analysis, applicable across various data types.

Findings

Inter-layer edges based on cross-horizontal visibility retain more information.

The method captures complementary information to intra-layer edges.

Experimental results demonstrate improved data analysis and insights.

Abstract

Multivariate time series analysis is a vital but challenging task, with multidisciplinary applicability, tackling the characterization of multiple interconnected variables over time and their dependencies. Traditional methodologies often adapt univariate approaches or rely on assumptions specific to certain domains or problems, presenting limitations. A recent promising alternative is to map multivariate time series into high-level network structures such as multiplex networks, with past work relying on connecting successive time series components with interconnections between contemporary timestamps. In this work, we first define a novel cross-horizontal visibility mapping between lagged timestamps of different time series and then introduce the concept of multilayer horizontal visibility graphs. This allows describing cross-dimension dependencies via inter-layer edges, leveraging the entire structure of multilayer networks. To this end, a novel parameter-free topological measure is proposed and common measures are extended for the multilayer setting. Our approach is general and applicable to any kind of multivariate time series data. We provide an extensive experimental evaluation with both synthetic and real-world datasets. We first explore the proposed methodology and the data properties highlighted by each measure, showing that inter-layer edges based on cross-horizontal visibility preserve more information than previous mappings, while also complementing the information captured by commonly used intra-layer edges. We then illustrate the applicability and validity of our approach in multivariate time series mining tasks, showcasing its potential for enhanced data analysis and insights.