Merlin：面向非固定缺失率下鲁棒多元时间序列预测的多视图表示学习
Merlin: Multi-View Representation Learning for Robust Multivariate Time Series Forecasting with Unfixed Missing Rates
AI总结：
针对多元时间序列预测中缺失值分布变化、现有模型鲁棒性不足的问题，提出包含离线知识蒸馏与多视图对比学习的Merlin方法，实现不同缺失率下的语义对齐，在保持精度的同时提升模型鲁棒性，经四个真实数据集验证效果优异。
摘要：
多元时间序列预测（Multivariate Time Series Forecasting, MTSF）旨在预测多个相互关联的时间序列的未来值。近年来，基于深度学习的MTSF模型因在挖掘MTS数据内的语义（全局与局部信息）方面表现出优异能力而受到广泛关注。然而，这类模型普遍易受数据采集器故障导致的缺失值影响。这些缺失值不仅会破坏MTS的语义，其分布还会随时间发生变化。但现有模型对这类问题缺乏鲁棒性，导致预测性能欠佳。为此，本文提出多视图表示学习方法Merlin，可帮助现有模型实现MTS中不同缺失率的不完整观测值与完整观测值之间的语义对齐。具体而言，Merlin包含两个核心模块：离线知识蒸馏与多视图对比学习。前者利用教师模型引导学生模型从不完整观测值中挖掘语义，使其接近从完整观测值中可获得的语义效果。后者通过从不同缺失率的不完整观测值构建的正负数据对进行学习，提升学生模型的鲁棒性，确保不同缺失率下的语义对齐。因此，Merlin能够在保持预测精度的同时，有效增强现有模型应对非固定缺失率的鲁棒性。在四个真实世界数据集上的实验证明了Merlin的优越性。
英文摘要：
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.
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。
