Title: FV-MgNet: Fully Connected V-cycle MgNet for Interpretable Time Series Forecasting. Abstract: 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, one of the most difficult tasks in forecasting problems. 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 it to the forecasting problems. Motivated by the V-cycle structure in MG, we further propose the FV-MgNet, a V-cycle version of 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 also develop ablation experiments to demonstrate the structure of FV-MgNet is the best choice among the many variants.