CCD: Capturing Cross-Correlations with Deformable Convolutional Networks for Multivariate Time Series Forecasting

The accuracy of Multivariate Time Series Forecasting relies on capturing precise channel correlations. Recent studies have primarily focused on capturing distinct channel relationships within each individual frequency and scale. However, complex channel dependencies are more prominently found in correlations across scales and frequencies. We term these relationships as Cross-Correlations. Drawing inspiration from Computer Vision, an efficient and effective strategy for capturing such 2D dependencies is to represent time series as Channel-Frequency and Channel-Scale views that possess image-like structures, and then use Convolutional Neural Networks to capture these complex Cross-Correlations.