CorrDCN: Decomposed Convolutional Network with Seasonal Autocorrelation 2D-Variation Modeling for Time Series Forecasting

Abstract

Time series forecasting plays an important role in numerous real-world domains. Considerable studies have been devoted to prediction by learning temporal features, utilizing improved variants of deep neural networks. However, the variable temporal patterns inherent in complex time series prohibit deep models from discovering reliable dependencies, impairing prediction accuracy. Going beyond previous models, we propose CorrDCN, a decomposed convolutional network with the capability of seasonal autocorrelation 2D-variation modeling. We design the Frequency Guided Decomposition block adaptively configured based on the input series. This facilitates precise series decomposition while allowing CorrDCN to personalize modeling for the decomposed components. Further, we utilize a concise cascaded MLP structure to progressively learn trend variations and integrate local features with global correlations to adequately model seasonal variations. In particular, to tackle the limitations of the 1D structure in simultaneous modeling, we represent seasonal variations in 2D space by reshaping a seasonal 2D tensor based on autocorrelation. This reshaping operation embeds the local features and global correlations of the seasonal series into the rows and columns of the 2D tensor, and thus such seasonal 2D variations can be easily captured by the Multi-scale Inception layers. CorrDCN shows competitive performance on six benchmark datasets. Compared to the mainstream prediction models TimesNet, Non-stationary Transformer and FEDformer, CorrDCN achieves averaged MSE reductions of 4.9%, 17.1% and 19.4%, respectively.