重新审视具有缺失值的多变量时间序列预测
Revisiting Multivariate Time Series Forecasting with Missing Values
AI总结：
本文提出CRIB框架，通过直接预测部分观测时间序列，解决缺失值对预测准确性的影响，提升多变量时间序列预测的鲁棒性。
摘要：
缺失值在现实世界的时间序列中很常见，多变量时间序列预测中的缺失值（MTSF-M）已成为确保可靠预测的关键研究领域。为了解决缺失数据的挑战，当前的方法开发了一种填充缺失值然后预测的框架，该框架使用填充模块填充缺失值，然后在填充后的数据上进行预测。然而，这种框架忽略了关键问题：没有缺失值的真实值，使填充过程容易出错，从而降低预测准确性。在本文中，我们进行了系统的经验研究，并发现没有直接监督的填充会破坏底层的数据分布，并主动降低预测准确性。为此，我们提出了一种范式转变，从填充转向直接从部分观察的时间序列进行预测。我们引入了Consistency-Regularized Information Bottleneck（CRIB），一种基于信息瓶颈原理的新框架。CRIB结合了统一变量注意机制和一致性正则化方案，以学习稳健的表示，从而过滤掉由缺失值引入的噪声，同时保留必要的预测信号。在四个真实世界数据集上的全面实验展示了CRIB的有效性，即使在高缺失率下也能准确预测。我们的代码可在https://github.com/Muyiiiii/CRIB上获得。
英文摘要：
Missing values are common in real-world time series, and multivariate time series forecasting with missing values (MTSF-M) has become a crucial area of research for ensuring reliable predictions. To address the challenge of missing data, current approaches have developed an imputation-then-prediction framework that uses imputation modules to fill in missing values, followed by forecasting on the imputed data. However, this framework overlooks a critical issue: there is no ground truth for the missing values, making the imputation process susceptible to errors that can degrade prediction accuracy. In this paper, we conduct a systematic empirical study and reveal that imputation without direct supervision can corrupt the underlying data distribution and actively degrade prediction accuracy. To address this, we propose a paradigm shift that moves away from imputation and directly predicts from the partially observed time series. We introduce Consistency-Regularized Information Bottleneck (CRIB), a novel framework built on the Information Bottleneck principle. CRIB combines a unified-variate attention mechanism with a consistency regularization scheme to learn robust representations that filter out noise introduced by missing values while preserving essential predictive signals. Comprehensive experiments on four real-world datasets demonstrate the effectiveness of CRIB, which predicts accurately even under high missing rates. Our code is available in https://github.com/Muyiiiii/CRIB.
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。
