Abstract
Multivariate Time Series Forecasting (MTSF) involves predicting future values of multiple interrelated time series. Recently, deep learning-based MTSF models have gained significant attention for their promising ability to mine semantics (global and local information) within MTS data. However, these models are pervasively susceptible to missing values caused by malfunctioning data collectors. These missing values not only disrupt the semantics of MTS, but their distribution also changes over time. Nevertheless, existing models lack robustness to such issues, leading to suboptimal forecasting performance. To this end, in this paper, we propose Multi-View Representation Learning (Merlin), which can help existing models achieve semantic alignment between incomplete observations with different missing rates and complete observations in MTS. Specifically, Merlin consists of two key modules: offline knowledge distillation and multi-view contrastive learning. The former utilizes a teacher model to guide a student model in mining semantics from incomplete observations, similar to those obtainable from complete observations. The latter improves the student model's robustness by learning from positive/negative data pairs constructed from incomplete observations with different missing rates, ensuring semantic alignment across different missing rates. Therefore, Merlin is capable of effectively enhancing the robustness of existing models against unfixed missing rates while preserving forecasting accuracy. Experiments on four real-world datasets demonstrate the superiority of Merlin.
Abstract (translated)
多元时间序列预测（MTSF）涉及对未来多个相互关联的时间序列值进行预测。近年来，基于深度学习的MTSF模型因其在挖掘多时间序列数据中的语义信息（全局和局部信息）方面表现出的巨大潜力而备受关注。然而，这些模型普遍容易受到因数据采集设备故障而导致的数据缺失的影响。这些缺失不仅会破坏多元时间序列的语义结构，其分布也会随时间变化。现有的模型对于这些问题缺乏鲁棒性，导致预测性能不佳。 为此，在本文中我们提出了多视角表示学习（Merlin），它可以协助现有模型在不同缺失率的不完整观测与完整观测之间实现语义对齐。具体而言，Merlin包括两个关键模块：离线知识蒸馏和多视角对比学习。前者利用一个教师模型引导学生模型从不完整的观测数据中挖掘出类似于从完整观测数据中可获取的语义信息。后者通过从具有不同缺失率的不完整观察数据构建的正/负数据对来提高学生的鲁棒性，确保在不同的缺失率下保持语义一致性。因此，Merlin能够有效地增强现有模型对于不可修复的缺失率变化问题的鲁棒性同时维持预测准确性。 实验结果表明，在四个真实世界的数据集上，Merlin表现出了优于其他方法的优势。
URL
https://arxiv.org/abs/2506.12459
