Cluster-aware attentive convolutional recurrent network for multivariate time-series forecasting

Multivariate time-series (MTS) forecasting plays a crucial role in various real-world applications, but the complex dependencies between time-series variables (i.e., inter-series dependencies) make this task extremely challenging. While most existing studies focus on modeling intra-series (temporal) dependencies by capturing long- and short-term patterns, they fail to explore and exploit the inter-series dependencies to enhance MTS forecasting. In this paper, we propose a Cluster-aware Attentive Convolutional Recurrent Network (CACRN) to capture both inter-series and intra-series dependencies in MTS data. Specifically, CACRN first introduces a cluster-aware variable representation module that separates irrelevant variables and captures the interaction between relevant variables to learn cluster-aware variable representations. Then, CACRN feeds these representations into parallel convolutional recurrent neural networks (CRNNs) to capture the short- and long-term temporal dependencies in a cluster-wise manner. Next, a cluster-aware attention mechanism is introduced to attend to temporal information in each cluster and co-attend all cluster information jointly to capture intra-cluster and inter-cluster dependencies for the downstream forecasting task. Our extensive experiments on six real-world datasets demonstrate that CACRN is effective and outperforms representative and state-of-the-art baselines. Our proposed method is suitable for a wide range of real-world data collections, especially those with clear dependencies of variables.