诊断多通道时间序列分类中的时间错位：基于最小描述长度的方法
Diagnosing Temporal Misalignment in Multichannel Time-Series Classification with Minimum Description Length
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- Lamarr Institute, TU Dortmund University（拉马尔研究所，多特蒙德工业大学）
- DAES Group, TU Dortmund University（DAES研究组，多特蒙德工业大学）
- Cyber-Physical Systems, RWTH Aachen（网络物理系统，亚琛工业大学）
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中文总结 AI 辅助
提出基于最小描述长度的无监督诊断方法，通过测量通道间编码效率检测多通道时间序列分类中的时间错位，无需重训练或参考对齐，实验验证可恢复部署漂移下的准确性并揭示基准数据集的系统性偏移。
中文摘要 AI 辅助
多通道时间序列分类通常假设传感器流是同步的，尽管延迟、时钟漂移和预处理可能在数据收集期间或部署后引入相对延迟。现有的同步解决方案通常依赖于特定硬件，且难以追溯应用。因此，同步问题可能在分类性能次优时仍未被检测到。我们提出了一种基于最小描述长度（MDL）的分类器和标签无关的诊断方法。我们的方法对传感器组应用候选时间偏移，并衡量通过其余通道的表示对一个组进行编码的效率。编码长度的增加表明该偏移破坏了共享的时间结构，而最小值则识别出数据最强烈支持的对齐。与学习的同步方法不同，该诊断方法既不需要重新训练，也不需要可信的对齐参考，因此可以测试训练和部署数据中的错位。在两个受控合成任务和九个真实世界数据集上的实验表明，该指标揭示了对齐结构，并能在诱导的部署漂移下恢复准确性。对整个数据集的审计进一步在包括FordChallenge、Opportunity、PAMAP2和UCIActivity在内的既定基准中识别出稳定的非零MDL最优值，揭示了传统模型评估无法暴露的潜在系统性偏移。因此，我们的方法为在整个时间序列学习流程中检测、理解和纠正时间错位提供了一个通用工具。我们的代码可在以下https URL下获取。
英文摘要
Multichannel time-series classification commonly assumes synchronized sensor streams, although latency, clock drift, and preprocessing can introduce relative delays during data collection or after deployment. Existing synchronization solutions are often hardware-specific and difficult to apply retrospectively. Consequently, synchronization problems may remain undetected while classification performance is suboptimal. We introduce a classifier- and label-free diagnostic based on minimum description length (MDL). Our method applies candidate temporal shifts to sensor groups and measures how efficiently one group can be encoded through a representation of the remaining channels. An increased codelength indicates that the shift destroys shared temporal structure, whereas the minimum identifies the alignment most strongly supported by the data. Unlike learned synchronization methods, the diagnostic requires neither retraining nor a trusted aligned reference and can therefore test both training and deployment data for misalignments. Experiments on two controlled synthetic tasks and nine real-world datasets show that the metric exposes alignment structure and can recover accuracy under induced deployment drift. A whole-dataset audit further identifies stable nonzero MDL optima in established benchmarks including FordChallenge, Opportunity, PAMAP2, and UCIActivity, revealing potential systematic offsets that conventional model evaluation does not expose. Our method thus provides a general-purpose tool for detecting, understanding, and correcting temporal misalignment throughout the time-series learning pipeline. Our code is available under https://github.com/sbuschjaeger/mdl-temporal-misalignment.
