IBN：一种用于变量缺失多元时间序列预测的可解释双向建模网络
IBN: An Interpretable Bidirectional-Modeling Network for Multivariate Time Series Forecasting with Variable Missing
AI总结：
针对变量缺失的多元时间序列预测，提出可解释双向建模网络IBN，集成不确定性感知插补与高斯核图卷积，实现最先进预测性能。
摘要：
多元时间序列预测（MTSF）常面临变量缺失的挑战，这阻碍了传统的时空图神经网络对变量间相关性的建模。虽然GinAR首次利用基于注意力的插补和自适应图学习解决了变量缺失问题，但其缺乏可解释性，且由于其简单的递归单元（RUs）而无法捕获更多潜在的时序模式。为克服这些局限，我们提出了可解释双向建模网络（IBN），集成了不确定性感知插补（UAI）和基于高斯核的图卷积（GGCN）。IBN利用MC Dropout估计重建值的不确定性，并采用不确定性加权策略以减轻高风险重建的影响。GGCN显式建模变量间的空间相关性，而双向RU增强了时序依赖建模。大量实验表明，IBN在各种缺失率场景下均取得了最先进的预测性能，为变量缺失的MTSF提供了更可靠且可解释的框架。代码可在以下网址获取：https://github.com/zhangth1211/NICLab-IBN。
英文摘要：
Multivariate time series forecasting (MTSF) often faces challenges from missing variables, which hinder conventional spatial-temporal graph neural networks in modeling inter-variable correlations. While GinAR addresses variable missing using attention-based imputation and adaptive graph learning for the first time, it lacks interpretability and fails to capture more latent temporal patterns due to its simple recursive units (RUs). To overcome these limitations, we propose the Interpretable Bidirectional-modeling Network (IBN), integrating Uncertainty-Aware Interpolation (UAI) and Gaussian kernel-based Graph Convolution (GGCN). IBN estimates the uncertainty of reconstructed values using MC Dropout and applies an uncertainty-weighted strategy to mitigate high-risk reconstructions. GGCN explicitly models spatial correlations among variables, while a bidirectional RU enhances temporal dependency modeling. Extensive experiments show that IBN achieves state-of-the-art forecasting performance under various missing-rate scenarios, providing a more reliable and interpretable framework for MTSF with missing variables. Code is available at: https://github.com/zhangth1211/NICLab-IBN.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
