Construction of time series causal network based on partial rank correlation

Abstract

Discovering causal relationships from time series data is a significant research area in data mining. To tackle the challenges of identifying and constructing causal networks in non-linear, high-dimensional time series data, we introduce the partial rank correlation coefficient and propose two new structure learning algorithms suitable for time-series causal network modeling. In this paper, we make three main contributions. Firstly, we demonstrate the suitability of the partial rank correlation as a criterion for independence testing. Secondly, by integrating the partial rank correlation with constraint-based causal discovery methods, we propose the TS-PRCS algorithm for temporal causal network discovery. Thirdly, we combine ideas from local learning, constraint-based methods, and score-based search to propose the TS-MCHC algorithm, which combines mixed constraints and hill-climbing search for time-series causal discovery. Finally, we validate the effectiveness of the TS-PRCS and TS-MCHC algorithms through experiments on both simulated and real-world data. Compared to existing methods, our proposed algorithms exhibit robust causal detection capabilities in diverse non-linear environments and demonstrate efficient performance in high-dimensional time series networks. The experimental results affirm the ability of our methods to effectively uncover causal relationships among time series data, presenting promising prospects for practical applications.