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.

翻译：缺失值在现实世界的时间序列中普遍存在，含缺失值的多元时间序列预测已成为确保预测可靠性的关键研究领域。为应对数据缺失的挑战，当前方法发展了一种先填补后预测的框架，该框架使用填补模块填充缺失值，随后对填补后的数据进行预测。然而，该框架忽视了一个关键问题：缺失值并无真实基准，这使得填补过程容易产生误差，进而降低预测精度。本文通过一项系统的实证研究发现，在缺乏直接监督的情况下进行填补可能会破坏底层数据分布，并显著损害预测准确性。为解决此问题，我们提出一种范式转变，即摒弃填补步骤，直接从部分观测到的时间序列进行预测。我们引入了基于信息瓶颈原理的新框架——一致性正则化信息瓶颈。该框架结合了统一变量注意力机制与一致性正则化方案，以学习能够滤除由缺失值引入的噪声，同时保留关键预测信号的鲁棒表示。在四个真实世界数据集上的全面实验证明了该框架的有效性，即使在高缺失率下也能实现准确预测。我们的代码公开于 https://github.com/Muyiiiii/CRIB。