FV-MgNet: Fully connected V-cycle MgNet for interpretable time series forecasting

Jianqing Zhu, Juncai He, Lian Zhang, Jinchao Xu

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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, 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 datasets 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.

Original languageEnglish (US)
Article number102005
JournalJournal of Computational Science
Volume69
DOIs
StatePublished - May 2023

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)
  • Modeling and Simulation

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