RTNet: relational-temporal classification network for multivariate time series

With its successful application to areas like speech and audio recognition, the Multivariate Time Series Classification (MTSC) has become one of the most essential problems in the time series analysis domain. However, existing solutions are suboptimal as they mainly focus on modeling the temporal information of Multivariate Time Series (MTS) but fall short of capturing the relationship among the univariate time series (UTS). To address this problem, we propose the Relational-Temporal Classification Network (RTNet) to combine the spatial relationship and temporal information of MTS. RTNet employs a graph attention layer to learn the relationship between the UTS and a bottleneck module along with GRU to extract the temporal dependency. We conducted extensive experiments on fifteen public datasets of UEA. RTNet outperforms seven state-of-the-art baseline models, demonstrating its superiority.