DCNet: Multivariate Time Series Forecasting via an Efficient Dual Context Network

Multivariate time series forecasting is of great significance in fields such as energy scheduling, traffic management, and financial analysis. This paper proposes the Dual Context (DC) technique and reconstructs the retrieval space of the attention mechanism based on this technique; at the same time, combined with a novel multilayer perceptron, DCNet is constructed to fully exploit the coupling features between global and local contexts. DCNet not only avoids the isolated modeling state of global periodic patterns and local evolution dynamics in previous studies, but also effectively improves the problem of blind coupling of variables in existing inverted architectures under complex environments by introducing a global reference baseline. DCNet achieves overall leading accuracy on 12 widely used real-world datasets. In addition, further experiments show that the DC technique not only performs well on models based on the Transformer architecture, but also significantly improves linear models, proving that the technique has good universality and cross-architecture transferability.