Kernel-based Joint Independence Tests for Multivariate Stationary and Non-stationary Time Series

• URL: http://arxiv.org/abs/2305.08529v3
• Date: Thu, 2 Nov 2023 11:43:59 GMT
• Title: Kernel-based Joint Independence Tests for Multivariate Stationary and Non-stationary Time Series
• Authors: Zhaolu Liu and Robert L. Peach and Felix Laumann and Sara Vallejo Mengod and Mauricio Barahona
• Abstract summary: We introduce kernel-based statistical tests of joint independence in multivariate time series. We show how the method robustly uncovers significant higher-order dependencies in synthetic examples. Our method can aid in uncovering high-order interactions in data.
• Score: 0.6749750044497732
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Multivariate time series data that capture the temporal evolution of interconnected systems are ubiquitous in diverse areas. Understanding the complex relationships and potential dependencies among co-observed variables is crucial for the accurate statistical modelling and analysis of such systems. Here, we introduce kernel-based statistical tests of joint independence in multivariate time series by extending the $d$-variable Hilbert-Schmidt independence criterion (dHSIC) to encompass both stationary and non-stationary processes, thus allowing broader real-world applications. By leveraging resampling techniques tailored for both single- and multiple-realisation time series, we show how the method robustly uncovers significant higher-order dependencies in synthetic examples, including frequency mixing data and logic gates, as well as real-world climate, neuroscience, and socioeconomic data. Our method adds to the mathematical toolbox for the analysis of multivariate time series and can aid in uncovering high-order interactions in data.

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