Deep Coupling Network For Multivariate Time Series Forecasting
Kun Yi Qi Zhang Hui He Kaize Shi Liang Hu Ning An Zhendong Niu
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.