ForecastGrapher: Redefining Multivariate Time Series Forecasting with Graph Neural Networks
<|reference_start|>ForecastGrapher: Redefining Multivariate Time Series Forecasting with Graph Neural Networks: 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.<|reference_end|>
@article{cai2024forecastgrapher:,
    title={ForecastGrapher: Redefining Multivariate Time Series Forecasting with
  Graph Neural Networks},
    author={Wanlin Cai, Kun Wang, Hao Wu, Xiaoxu Chen, Yuankai Wu},
    journal={arXiv preprint arXiv:2405.18036},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.18036},
    primaryClass={cs.LG}
}