Regime Identification for Improving Causal Analysis in Non-stationary Timeseries

TL;DR

This paper introduces a regime identification method that segments non-stationary multivariate time series into stable regimes using Riemannian covariance analysis, enhancing causal discovery in complex data.

Contribution

The study presents a novel regime identification technique based on Riemannian covariance analysis to improve causal analysis in non-stationary time series.

Findings

Regime-wise causal analysis outperforms traditional methods on synthetic data.

The approach successfully uncovers causal structures in climate-ecosystem data.

Regime segmentation enhances understanding of dynamic causal relationships.

Abstract

Time series data from real-world systems often display non-stationary behavior, indicating varying statistical characteristics over time. This inherent variability poses significant challenges in deciphering the underlying structural relationships within the data, particularly in correlation and causality analyses, model stability, etc. Recognizing distinct segments or regimes within multivariate time series data, characterized by relatively stable behavior and consistent statistical properties over extended periods, becomes crucial. In this study, we apply the regime identification (RegID) technique to multivariate time series, fundamentally designed to unveil locally stationary segments within data. The distinguishing features between regimes are identified using covariance matrices in a Riemannian space. We aim to highlight how regime identification contributes to improving the discovery of causal structures from multivariate non-stationary time series data. Our experiments, encompassing both synthetic and real-world datasets, highlight the effectiveness of regime-wise time series causal analysis. We validate our approach by first demonstrating improved causal structure discovery using synthetic data where the ground truth causal relationships are known. Subsequently, we apply this methodology to climate-ecosystem dataset, showcasing its applicability in real-world scenarios.