Graph correlated attention recurrent neural network for multivariate time series forecasting

Abstract

Multivariate time series(MTS) forecasting is an urgent problem for numerous valuable applications. At present, attention-based methods can relieve recurrent neural networks’ limitations in MTS forecasting that are hard to focus on key information and capture long-term dependencies, but they fail to learn the time-varying pattern based on the reliable interaction. To reinforce the memory ability of key features across time, we propose a Graph Correlated Attention Recurrent Neural Network(GCAR). GCAR first nests Feature-level attention in the graph attention module to complement external feature representations on the extraction of multi-head temporal correlations. Then Multi-level attention is designed to add target factors’ impact on the selection of external correlation and achieve a fine-grained distinction of external features’ contribution. To better capture different series’ continuous dynamic changes, two parallel LSTMs are respectively applied to learn historical target series and external feature representations’ temporal dependencies. Finally, a fusion gate is employed to balance their information conflicts. The performance of GCAR model is tested on 4 datasets, and results show GCAR model performs the most stable and greatest predictive accuracy as the increasing of predicted horizons compared with state-of-the-art models even if the multivariate time series present strong volatility and randomness.