  By investigating iterative methods for a constrained linear model, we propose
a new class of fully connected V-cycle MgNet for long-term time series
forecasting, which is one of the most difficult tasks in forecasting. MgNet is
a CNN model that was proposed for image classification based on the multigrid
(MG) methods for solving discretized partial differential equations (PDEs). We
replace the convolutional operations with fully connected operations in the
existing MgNet and then apply them to forecasting problems. Motivated by the
V-cycle structure in MG, we further propose the FV-MgNet, a V-cycle version of
the fully connected MgNet, to extract features hierarchically. By evaluating
the performance of FV-MgNet on popular data sets and comparing it with
state-of-the-art models, we show that the FV-MgNet achieves better results with
less memory usage and faster inference speed. In addition, we develop ablation
experiments to demonstrate that the structure of FV-MgNet is the best choice
among the many variants.
