  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.
