  Long-term time series forecasting (LTSF) is a crucial aspect of modern
society, playing a pivotal role in facilitating long-term planning and
developing early warning systems. While many Transformer-based models have
recently been introduced for LTSF, a doubt have been raised regarding the
effectiveness of attention modules in capturing cross-time dependencies. In
this study, we design a mask-series experiment to validate this assumption and
subsequently propose the "Cross-variable Linear Integrated ENhanced Transformer
for Multivariate Long-Term Time Series Forecasting" (Client), an advanced model
that outperforms both traditional Transformer-based models and linear models.
Client employs linear modules to learn trend information and attention modules
to capture cross-variable dependencies. Meanwhile, it simplifies the embedding
and position encoding layers and replaces the decoder module with a projection
layer. Essentially, Client incorporates non-linearity and cross-variable
dependencies, which sets it apart from conventional linear models and
Transformer-based models. Extensive experiments with nine real-world datasets
have confirmed the SOTA performance of Client with the least computation time
and memory consumption compared with the previous Transformer-based models. Our
code is available at https://github.com/daxin007/Client.
