Evolutionary Neural Architecture Search for Multivariate Time Series Forecasting

Multivariate time series forecasting is of great importance in a diverse range of domains. In recent years, a variety of spatial-temporal graph neural networks (STGNNs) have been proposed to address this task and achieved promising results. However, these networks are typically handcrafted and require extensive human expertise. Additionally, the temporal and spatial dependencies hidden within different scenarios vary, making it difficult for them to adapt to different scenarios. In this paper, we propose an evolutionary neural architecture search framework, entitled EMTSF, for automated STGNN design. Specifically, we employ fine-grained neural architecture search into both the spatial convolution module and the temporal convolution module. For the spatial convolution search space, various feature filtering and neighbor aggregation operations are employed to find the most suitable message-passing mechanism for modeling the spatial dependencies. For the temporal convolution search space, gated temporal convolutions with different kernel sizes are utilized to best capture temporal dependencies with various ranges. The spatial convolution module and temporal convolution module are jointly optimized with the proposed evolutionary search algorithm to heuristically identify the optimal STGNN architecture. Extensive experiments on four commonly used benchmark datasets show EMTSF achieves promising performance compared with the state-of-the-art methods, which demonstrates the effectiveness of the proposed framework.