评估时间序列异常检测方法，发现主流模型效果有限。
TS-OOD: Evaluating Time-Series Out-of-Distribution Detection and Prospective Directions for Progress
- 跨数据集测试多种时序模型与增强策略
- 多数先进方法在时序数据上表现不佳
- 基于深度特征建模的方法更具潜力
检测分布外（OOD）数据是机器学习模型部署中的基本挑战。从安全角度，分布外测试数据可能导致高度自信却错误的预测，从而损害模型可靠性。尽管计算机视觉和自然语言处理领域已开发出大量OOD检测模型，但它们在时间序列数据领域的适应性仍有限且研究不足。而时间序列数据广泛应用于制造与安防场景，其OOD检测至关重要。本文通过全面分析无模态依赖的OOD检测算法，评估了多个多变量时间序列数据集、深度学习架构、时序专用数据增强方法及损失函数。结果表明：1）多数当前最先进的OOD方法在时间序列数据上表现有限；2）基于深度特征建模的OOD方法对时间序列检测更具优势，为未来算法发展指明了方向。
原文摘要 · Abstract (English)
Detecting out-of-distribution (OOD) data is a fundamental challenge in the deployment of machine learning models. From a security standpoint, this is particularly important because OOD test data can result in misleadingly confident yet erroneous predictions, which undermine the reliability of the deployed model. Although numerous models for OOD detection have been developed in computer vision and language, their adaptability to the time-series data domain remains limited and under-explored. Yet, time-series data is ubiquitous across manufacturing and security applications for which OOD is essential. This paper seeks to address this research gap by conducting a comprehensive analysis of modality-agnostic OOD detection algorithms. We evaluate over several multivariate time-series datasets, deep learning architectures, time-series specific data augmentations, and loss functions. Our results demonstrate that: 1) the majority of state-of-the-art OOD methods exhibit limited performance on time-series data, and 2) OOD methods based on deep feature modeling may offer greater advantages for time-series OOD detection, highlighting a promising direction for future time-series OOD detection algorithm development.
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