TimeEval: A Benchmarking Toolkit for Time Series Anomaly Detection Algorithms

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Abstract

Detecting anomalous subsequences in time series is an important task in time series analytics because it serves the identification of special events, such as production faults, delivery bottlenecks, system defects, or heart flicker. Consequently, many algorithms have been developed for the automatic detection of such anomalous patterns. The enormous number of approaches (i.e., more than 158 as of today), the lack of properly labeled test data, and the complexity of time series anomaly benchmarking have, though, led to a situation where choosing the best detection technique for a given anomaly detection task is a difficult challenge. In this demonstration, we present TimeEval, an extensible, scalable and automatic benchmarking toolkit for time series anomaly detection algorithms. TimeEval includes an extensive data generator and supports both interactive and batch evaluation scenarios. With our novel toolkit, we aim to ease the evaluation effort and help the community to provide more meaningful evaluations.