Deep Coupling Network For Multivariate Time Series Forecasting

TL;DR

This paper introduces DeepCN, a deep coupling network that captures multi-order intra- and inter-series relationships in multivariate time series data, significantly improving forecasting accuracy over existing methods.

Contribution

The paper proposes a novel deep coupling network that explicitly models multi-order intra- and inter-series relationships using mutual information, enhancing multivariate time series forecasting.

Findings

DeepCN outperforms state-of-the-art baselines on seven real-world datasets.

The coupling mechanism effectively captures complex multi-order relationships.

The model achieves superior forecasting accuracy across diverse applications.

Abstract

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.