MCformer: Multivariate Time Series Forecasting With Mixed-Channels Transformer
Wenyong Han, Tao Zhu, Liming Chen, Huansheng Ning, Yang Luo, Yaping Wan
Abstract
The massive generation of time-series data by large-scale Internet of Things (IoT) devices necessitates the exploration of more effective models for multivariate time-series forecasting. In previous models, there was a predominant use of the channel dependence (CD) strategy (where each channel represents a univariate sequence). Current state-of-the-art (SOTA) models primarily rely on the channel independence (CI) strategy. The CI strategy treats channel multichannel series as separate single-channel series, expanding the data set to improve generalization performance and avoiding interchannel correlation that disrupts long-term features. However, the CI strategy faces the challenge of interchannel correlation forgetting. To address this issue, we propose an innovative Mixed Channels strategy, combining the data expansion advantages of the CI strategy with the ability to mitigate interchannel correlation forgetting. Based on this strategy, we introduce MCformer, a multivariate time-series forecasting model with mixed channel features. The model blends a specific number of channels, leveraging an attention mechanism to effectively capture interchannel correlation information when modeling long-term features. Experimental results demonstrate that the Mixed Channels strategy outperforms pure CI strategy in multivariate time-series forecasting tasks.