Multi-variate time series forecasting plays a crucial
role in addressing key tasks across various domains, such as early
warning, pre-planning, resource scheduling, and other critical
tasks. Thus, accurate multi-variate time series forecasting is
of significant importance in guiding practical applications and
facilitating these essential tasks. Recently, Transformer-based
multi-variate time series forecasting models have demonstrated
tremendous potential due to their outstanding performance in
long-term time predictions. However, Transformer-based models
for multi-variate time series forecasting often come with high
time complexity and computational costs. Therefore, we propose
a low time complexity model called Fourier U-shaped Network
(F-UNet) for multi-variate time series forecasting, which is non-
Transformer based. Specifically, F-UNet is composed of low time
complexity neural network components, such as Fourier neural
operator and feed-forward neural network, arranged in a Ushaped
architecture. F-UNet conducts channel and temporal
modeling separately for the multi-variate time series. The UNet
constructed based on Fourier neural operators is employed
to achieve channel interactions, while linear layers are used to realize
temporal interactions. Experimental results on several realworld
datasets demonstrate that F-UNet outperforms existing
Transformer-based models with higher efficiency in multi-variate
time series forecasting.