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.

ForecastGrapher 是一个框架，将多变量时间序列预测重新构想为节点回归任务，通过生成自定义节点嵌入、构建自适应邻接矩阵和增强节点特征分布的表达能力，引入 GFC-GNN 模型来捕捉复杂的时间动态和跨序列的相关性，通过大量实验证明其在多变量时间序列预测领域超过了强基线和领先的已发表技术。