CATN: Cross Attentive Tree-Aware Network for Multivariate Time Series Forecasting

Modeling complex hierarchical and grouped feature interaction in the multivariate time series data is indispensable to comprehending the data dynamics and predicting the future condition. The implicit feature interaction and highdimensional data make multivariate forecasting very challenging. Many existing works did not put more emphasis on exploring explicit correlation among multiple time-series data, and complicated models are designed to capture longand short-range patterns with the aid of attention mechanisms. In this work, we think that a pre-defined graph or a general learning method is difficult due to its irregular structure. Hence, we present CATN, an end-to-end model of Cross Attentive Tree-aware Network to jointly capture the interseries correlation and intra-series temporal patterns. We first construct a tree structure to learn hierarchical and grouped correlation and design an embedding approach that can pass a dynamic message to generalize implicit but interpretable cross features among multiple time series. Next in the temporal aspect, we propose a multi-level dependency learning mechanism including global&local learning and cross attention mechanism, which can combine long-range dependencies, short-range dependencies as well as cross dependencies at different time steps. The extensive experiments on different datasets from real-world show the effectiveness and robustness of the method we proposed when compared with existing state-of-the-art methods.