MCformer: Multivariate Time Series Forecasting with Mixed-Channels
  Transformer
Wenyong Han Tao Zhu Member Liming Chen Huansheng Ning Yang Luo Yaping Wan
The massive generation of time-series data by largescale 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 all channels as a single channel, expanding the dataset to improve
generalization performance and avoiding inter-channel 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 counteract inter-channel 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 inter-channel 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.