ForecastGrapher: Redefining Multivariate Time Series Forecasting with Graph Neural Networks

• URL: http://arxiv.org/abs/2405.18036v1
• Date: Tue, 28 May 2024 10:40:20 GMT
• Title: ForecastGrapher: Redefining Multivariate Time Series Forecasting with Graph Neural Networks
• Authors: Wanlin Cai, Kun Wang, Hao Wu, Xiaoxu Chen, Yuankai Wu,
• Abstract summary: We present ForecastGrapher, a framework for capturing the intricate temporal dynamics and inter-series correlations. Our approach is underpinned by three pivotal steps: generating custom node embeddings to reflect the temporal variations within each series; constructing an adaptive adjacency matrix to encode the inter-series correlations; and thirdly, augmenting the GNNs' expressive power by diversifying the node feature distribution.
• Score: 9.006068771300377
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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