Edge-Varying Fourier Graph Networks for Multivariate Time Series Forecasting

by   Kun Yi, et al.
University of Technology Sydney
Beijing Institute of Technology

The key problem in multivariate time series (MTS) analysis and forecasting aims to disclose the underlying couplings between variables that drive the co-movements. Considerable recent successful MTS methods are built with graph neural networks (GNNs) due to their essential capacity for relational modeling. However, previous work often used a static graph structure of time-series variables for modeling MTS failing to capture their ever-changing correlations over time. To this end, a fully-connected supra-graph connecting any two variables at any two timestamps is adaptively learned to capture the high-resolution variable dependencies via an efficient graph convolutional network. Specifically, we construct the Edge-Varying Fourier Graph Networks (EV-FGN) equipped with Fourier Graph Shift Operator (FGSO) which efficiently performs graph convolution in the frequency domain. As a result, a high-efficiency scale-free parameter learning scheme is derived for MTS analysis and forecasting according to the convolution theorem. Extensive experiments show that EV-FGN outperforms state-of-the-art methods on seven real-world MTS datasets.


Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks

Modeling multivariate time series has long been a subject that has attra...

Learning the Evolutionary and Multi-scale Graph Structure for Multivariate Time Series Forecasting

Recent studies have shown great promise in applying graph neural network...

Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting

Multivariate time-series forecasting plays a crucial role in many real-w...

Modeling Complex Spatial Patterns with Temporal Features via Heterogenous Graph Embedding Networks

Multivariate time series (MTS) forecasting is an important problem in ma...

Learning Informative Representation for Fairness-aware Multivariate Time-series Forecasting: A Group-based Perspective

Multivariate time series (MTS) forecasting has penetrated and benefited ...

GAETS: A Graph Autoencoder Time Series Approach Towards Battery Parameter Estimation

Lithium-ion batteries are powering the ongoing transportation electrific...

Multivariate Time Series Forecasting Based on Causal Inference with Transfer Entropy and Graph Neural Network

Multivariate time series (MTS) forecasting is an important problem in ma...

Please sign up or login with your details

Forgot password? Click here to reset