ForecastGrapher: Redefining Multivariate Time Series Forecasting with Graph Neural Networks

TL;DR

ForecastGrapher introduces a novel graph neural network framework that models multivariate time series as a node regression problem, effectively capturing inter-series correlations and temporal dynamics to improve forecasting accuracy.

Contribution

The paper presents ForecastGrapher, a new GNN-based framework with a specialized GFC-GNN model that enhances expressive power for multivariate time series forecasting.

Findings

Outperforms strong baseline models in accuracy

Effectively captures inter-series correlations

Demonstrates robustness through extensive experiments

Abstract

The challenge of effectively learning inter-series correlations for multivariate time series forecasting remains a substantial and unresolved problem. Traditional deep learning models, which are largely dependent on the Transformer paradigm for modeling long sequences, often fail to integrate information from multiple time series into a coherent and universally applicable model. To bridge this gap, our paper presents ForecastGrapher, a framework reconceptualizes multivariate time series forecasting as a node regression task, providing a unique avenue for capturing the intricate temporal dynamics and inter-series correlations. Our approach is underpinned by three pivotal steps: firstly, generating custom node embeddings to reflect the temporal variations within each series; secondly, constructing an adaptive adjacency matrix to encode the inter-series correlations; and thirdly, augmenting the GNNs' expressive power by diversifying the node feature distribution. To enhance this expressive power, we introduce the Group Feature Convolution GNN (GFC-GNN). This model employs a learnable scaler to segment node features into multiple groups and applies one-dimensional convolutions with different kernel lengths to each group prior to the aggregation phase. Consequently, the GFC-GNN method enriches the diversity of node feature distribution in a fully end-to-end fashion. Through extensive experiments and ablation studies, we show that ForecastGrapher surpasses strong baselines and leading published techniques in the domain of multivariate time series forecasting.