指出时序异常检测研究与工业实践脱节，提出五大待改进方向。
Open Challenges in Time Series Anomaly Detection: An Industry Perspective
- 基于云环境部署系统，提炼出五类实际场景需求
- 强调流式处理、人机协同、条件异常等关键挑战
- 适合关注工业落地的算法研究者和工程师
当前时序异常检测研究采用的定义忽略了实践中关键应用特征。本文基于对云环境中部署系统的调研，列出若干具有实际意义但被忽视或未涵盖的研究领域：流式算法、人机协同场景、点过程建模、条件异常检测以及时序群体分析。这些方向对理论与应用研究均具启发性，也推动新数据集与基准的构建，呼吁学界重视工业真实场景中的挑战。
原文摘要 · Abstract (English)
Current research in time-series anomaly detection is using definitions that miss critical aspects of how anomaly detection is commonly used in practice. We list several areas that are of practical relevance and that we believe are either under-investigated or missing entirely from the current discourse. Based on an investigation of systems deployed in a cloud environment, we motivate the areas of streaming algorithms, human-in-the-loop scenarios, point processes, conditional anomalies and populations analysis of time series. This paper serves as a motivation and call for action, including opportunities for theoretical and applied research, as well as for building new dataset and benchmarks.
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