AnomalyBench: An Open Benchmark for Explainable Anomaly Detection

Access to high-quality data repositories and benchmarks have been instrumental in advancing the state of the art in many domains, as they provide the research community a common ground for training, testing, evaluating, comparing, and experimenting with novel machine learning models. Lack of such community resources for anomaly detection (AD) severely limits progress. In this report, we present AnomalyBench, the first comprehensive benchmark for explainable AD over high-dimensional (2000+) time series data. AnomalyBench has been systematically constructed based on real data traces from ~100 repeated executions of 10 large-scale stream processing jobs on a Spark cluster. 30+ of these executions were disturbed by introducing ~100 instances of different types of anomalous events (e.g., misbehaving inputs, resource contention, process failures). For each of these anomaly instances, ground truth labels for the root-cause interval as well as those for the effect interval are available, providing a means for supporting both AD tasks and explanation discovery (ED) tasks via root-cause analysis. We demonstrate the key design features and practical utility of AnomalyBench through an experimental study with three state-of-the-art semi-supervised AD techniques.