GAN-Based Temporal Association Rule Mining on Multivariate Time Series Data

Guoliang He, Lifang Dai, Zhiwen Yu, C. L. Philip Chen

Abstract: Feature mining is a challenging work in the field of multivariate time series (MTS) data mining. Traditional methods suffer from three major issues. 1) Learned shapelets may seriously diverge from original subsequences since learning methods do not restrain the learned ones similar to raw sequences, which reduces interpretability. 2) Existing rule mining methods just generate association rules based on feature combination of different variables without considering temporal relations among features, which could not adequately express the essential characteristics of MTS data. 3) Most deep learning methods only mine global and high-level features of MTS data, which affects interpretability. To address these issues, we propose a temporal association rule mining method based on Generative Adversarial Network (GAN) called TAR-GAN. First, a shapelet mining method based on GAN (SGAN) is advanced to discover dataset-level and sample-level shapelets of all variables in MTS data. Second, a Temporal Graph based Rule Mining method (TGRM) is introduced to discover temporal association rules based on the temporal relationships among shapelets of different variables. Meanwhile, a Fast Convolution-based Similarity Measure method s (FCSM) is introduced to measure the similarity between MTS samples and temporal association rules. Furthermore, an adversarial training strategy is introduced to ensure the effectiveness and stability of generated temporal association rules, which could reflect the essential characteristics of MTS data. Extensive experiments on 12 datasets show the effectiveness and efficiency of our method.