GinAR: An End-To-End Multivariate Time Series Forecasting Model Suitable for Variable Missing
<|reference_start|>GinAR: An End-To-End Multivariate Time Series Forecasting Model Suitable for Variable Missing: Multivariate time series forecasting (MTSF) is crucial for decision-making to precisely forecast the future values/trends, based on the complex relationships identified from historical observations of multiple sequences. Recently, Spatial-Temporal Graph Neural Networks (STGNNs) have gradually become the theme of MTSF model as their powerful capability in mining spatial-temporal dependencies, but almost of them heavily rely on the assumption of historical data integrity. In reality, due to factors such as data collector failures and time-consuming repairment, it is extremely challenging to collect the whole historical observations without missing any variable. In this case, STGNNs can only utilize a subset of normal variables and easily suffer from the incorrect spatial-temporal dependency modeling issue, resulting in the degradation of their forecasting performance. To address the problem, in this paper, we propose a novel Graph Interpolation Attention Recursive Network (named GinAR) to precisely model the spatial-temporal dependencies over the limited collected data for forecasting. In GinAR, it consists of two key components, that is, interpolation attention and adaptive graph convolution to take place of the fully connected layer of simple recursive units, and thus are capable of recovering all missing variables and reconstructing the correct spatial-temporal dependencies for recursively modeling of multivariate time series data, respectively. Extensive experiments conducted on five real-world datasets demonstrate that GinAR outperforms 11 SOTA baselines, and even when 90% of variables are missing, it can still accurately predict the future values of all variables.<|reference_end|>
@article{yu2024ginar:,
    title={GinAR: An End-To-End Multivariate Time Series Forecasting Model Suitable
  for Variable Missing},
    author={Chengqing Yu, Fei Wang, Zezhi Shao, Tangwen Qian, Zhao Zhang, Wei Wei,
  Yongjun Xu},
    journal={arXiv preprint arXiv:2405.11333},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.11333},
    primaryClass={cs.LG cs.AI}
}