TodyNet: Temporal Dynamic Graph Neural Network for Multivariate Time Series Classification

Understand

Multivariate time series classification (MTSC) is an important data mining task, which can be effectively solved by popular deep learning technology.

• Unfortunately, the existing deep learning-based methods neglect the hidden dependencies in different dimensions and also rarely consider the unique dynamic features of time series, which lack sufficient feature extraction capability to obtain satisfactory classification accuracy.
• To address this problem, we propose a novel temporal dynamic graph neural network (TodyNet) that can extract hidden spatio-temporal dependencies without undefined graph structure.
• It enables information flow among isolated but implicit interdependent variables and captures the associations between different time slots by dynamic graph mechanism, which further improves the classification performance of the model.