解决变量缺失的多变量时间序列预测，提升准确率与可解释性。
IBN: An Interpretable Bidirectional-Modeling Network for Multivariate Time Series Forecasting with Variable Missing
- 引入双向递归单元与不确定性感知插补，增强时序建模能力。
- 在不同缺失率下表现优于现有方法，最高提升6.2%（MAE）。
- 适合需要高可靠性和可解释性的工业场景时间序列分析。
多变量时间序列预测常受变量缺失影响，传统时空图神经网络难以建模变量间相关性。尽管GinAR首次通过注意力插补和自适应图学习应对缺失问题，但缺乏可解释性，且因简单递归单元无法捕捉深层时序模式。为此，本文提出可解释双向建模网络（IBN），融合不确定性感知插补（UAI）与高斯核图卷积（GGCN）。IBN利用MC Dropout估计重建值不确定性，采用不确定性加权策略降低高风险重建误差；GGCN显式建模变量间空间相关性，双向递归单元强化时序依赖性。大量实验表明，IBN在多种缺失率下均达到最优预测性能，为变量缺失的多变量时间序列预测提供了更可靠、可解释的框架。代码已开源：https://github.com/zhangth1211/NICLab-IBN。
原文摘要 · Abstract (English)
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.
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