MOSPAT: AutoML based Model Selection and Parameter Tuning for Time Series Anomaly Detection
This addresses the problem of inefficient manual tuning and lack of ground truth for anomaly detection in large-scale organizational monitoring, though it is incremental as it builds on existing AutoML and generative modeling techniques.
The paper tackles the challenge of scaling anomaly detection for time series across diverse use cases in large organizations by introducing MOSPAT, an AutoML-based system that automates model selection and parameter tuning, and it consistently outperforms single-algorithm approaches in experiments.
Organizations leverage anomaly and changepoint detection algorithms to detect changes in user behavior or service availability and performance. Many off-the-shelf detection algorithms, though effective, cannot readily be used in large organizations where thousands of users monitor millions of use cases and metrics with varied time series characteristics and anomaly patterns. The selection of algorithm and parameters needs to be precise for each use case: manual tuning does not scale, and automated tuning requires ground truth, which is rarely available. In this paper, we explore MOSPAT, an end-to-end automated machine learning based approach for model and parameter selection, combined with a generative model to produce labeled data. Our scalable end-to-end system allows individual users in large organizations to tailor time-series monitoring to their specific use case and data characteristics, without expert knowledge of anomaly detection algorithms or laborious manual labeling. Our extensive experiments on real and synthetic data demonstrate that this method consistently outperforms using any single algorithm.