ChannelMixer: A Hybrid CNN-Transformer Framework for Enhanced Multivariate Long-Term Time Series Forecasting

Abstract

Multivariate long-term time series forecasting is a challenging task, that analyzes historical data across multiple variables to predict future data. To solve this problem, we proposed a new ChannelMixer model that combines channel dependency and channel independency learning strategies. In channel dependency learning, we proposed an Adaptive Variable and Short-Term Temporal Feature Extraction block, which uses the convolutional neural network and Couple-Slice Transform to capture complex short-term interactions. Concurrently, a channel-independent Transformer learning is used to capture long-term periodic information across the intra-subsequences dimension of time series data. Additionally, a linear head with channel independency is included to directly predict long-term trends, ensuring a balance between short-term dynamics and long-term forecasting. Finally, we make the predictions by fusing local-term dynamics features and global trends. Extensive experiments show that ChannelMixer outperforms state-of-the-art models in predictive accuracy.