  Multivariate time series forecasting has been widely used in various
practical scenarios. Recently, Transformer-based models have shown significant
potential in forecasting tasks due to the capture of long-range dependencies.
However, recent studies in the vision and NLP fields show that the role of
attention modules is not clear, which can be replaced by other token
aggregation operations. This paper investigates the contributions and
deficiencies of attention mechanisms on the performance of time series
forecasting. Specifically, we find that (1) attention is not necessary for
capturing temporal dependencies, (2) the entanglement and redundancy in the
capture of temporal and channel interaction affect the forecasting performance,
and (3) it is important to model the mapping between the input and the
prediction sequence. To this end, we propose MTS-Mixers, which use two
factorized modules to capture temporal and channel dependencies. Experimental
results on several real-world datasets show that MTS-Mixers outperform existing
Transformer-based models with higher efficiency.
