Deep Coupling Network For Multivariate Time Series Forecasting
This addresses the problem of accurate forecasting in applications like finance or weather by improving modeling of complex time series interactions, though it appears incremental as it builds on existing relationship modeling approaches.
The paper tackles multivariate time series forecasting by proposing DeepCN, a deep coupling network that simultaneously captures multi-order intra- and inter-series relationships, achieving superior performance on seven real-world datasets compared to state-of-the-art baselines.
Multivariate time series (MTS) forecasting is crucial in many real-world applications. To achieve accurate MTS forecasting, it is essential to simultaneously consider both intra- and inter-series relationships among time series data. However, previous work has typically modeled intra- and inter-series relationships separately and has disregarded multi-order interactions present within and between time series data, which can seriously degrade forecasting accuracy. In this paper, we reexamine intra- and inter-series relationships from the perspective of mutual information and accordingly construct a comprehensive relationship learning mechanism tailored to simultaneously capture the intricate multi-order intra- and inter-series couplings. Based on the mechanism, we propose a novel deep coupling network for MTS forecasting, named DeepCN, which consists of a coupling mechanism dedicated to explicitly exploring the multi-order intra- and inter-series relationships among time series data concurrently, a coupled variable representation module aimed at encoding diverse variable patterns, and an inference module facilitating predictions through one forward step. Extensive experiments conducted on seven real-world datasets demonstrate that our proposed DeepCN achieves superior performance compared with the state-of-the-art baselines.