🤖 AI Summary
Current time-series anomaly detection research predominantly focuses on static, batch-processing settings, overlooking critical industrial requirements—namely, streaming computation, human-in-the-loop interaction, point-process modeling, conditional anomaly identification, and multi-series collective analysis. Method: This paper systematically identifies five long-neglected industrial challenges and introduces two novel paradigms: *conditional anomalies* and *collective time-series analysis*. We propose an interpretable, interactive, and scalable framework integrating stream processing architecture, interactive feedback mechanisms, point-process statistical modeling, conditional dependency graph learning, and collective clustering techniques. Contribution/Results: Our work establishes a practical industrial adoption roadmap, catalyzes the development of new benchmark datasets and standardized evaluation protocols, and bridges the gap between theoretical research and real-world deployment in time-series anomaly detection.
📝 Abstract
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.