Causal Discovery for Nonstationary Nonlinear Time Series Data Using Just-In-Time Modeling

Abstract

Causal discovery from multivariate time series data is becoming important as the increase of analysis for IoT data. However, it is not easy to identify the causal structure from such data due to their non-stationarity or distribution shifts using conventional linear causal discovery methods. The application of non-linear methods is also limited because of their computational complexity. To address these problems, we propose a causal discovery method based on the Linear Non-Gaussian Acyclic Model (LiNGAM) and the Just-In-Time (JIT) framework. The method estimates a local linear structural causal model from neighboring samples of the past data every time a new input sample is given. We show the effectiveness of the method by numerical experiments using artificial data with non-stationarity and non-linearity.