The current state of machine learning scholarship in Timeseries Anomaly
Detection (TAD) is plagued by the persistent use of flawed evaluation metrics,
inconsistent benchmarking practices, and a lack of proper justification for the
choices made in novel deep learning-based model designs. Our paper presents a
critical analysis of the status quo in TAD, revealing the misleading track of
current research and highlighting problematic methods, and evaluation
practices. Our position advocates for a shift in focus from pursuing only the
novelty in model design to improving benchmarking practices, creating
non-trivial datasets, and placing renewed emphasis on studying the utility of
model architectures for specific tasks. Our findings demonstrate the need for
rigorous evaluation protocols, the creation of simple baselines, and the
revelation that state-of-the-art deep anomaly detection models effectively
learn linear mappings. These findings suggest the need for more exploration and
development of simple and interpretable TAD methods. The increment of model
complexity in the state-of-the-art deep-learning based models unfortunately
offers very little improvement. We offer insights and suggestions for the field
to move forward.

当前时间序列异常检测（TAD）研究中存在评估指标缺陷、不一致的基准测试实践以及对新颖深度学习模型设计选择的缺乏合理的论证。本文对 TAD 中的现状进行了批判性分析，揭示了当前研究的误导轨迹，并突出了存在问题的方法及评估实践。我们的观点主张从仅追求模型设计的新颖性转向改进基准测试实践、构建非平凡数据集，并重新强调研究模型架构在特定任务中的实用性。我们的发现表明需要严格的评估协议、创建简单的基线，并揭示现有最先进的深度异常检测模型实际上只是学习线性映射。这些发现表明需要更多探索和开发简单且可解释的 TAD 方法。遗憾的是，现有最先进的深度学习模型的复杂性增加带来的改进很少。我们提供对该领域前进的见解和建议。