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Conference Papers Year : 2024

ADecimo: Model Selection for Time Series Anomaly Detection

Paul Boniol
Emmanouil Sylligardos
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John Paparrizos
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Abstract

Anomaly detection is a fundamental task for time-series analytics with important implications for the downstream performance of many applications. Despite increasing academic interest and the large number of methods proposed in the literature, recent benchmark and evaluation studies demonstrated that there exists no single best anomaly detection method when applied to heterogeneous time series datasets. Therefore, the only scalable and viable solution to solve anomaly detection over very different time series collected from diverse domains is to propose a model selection method that will choose, based on time series characteristics, the best anomaly detection method to run. This paper describes ADecimo, a modular and extensible web application that helps users understand the performance of time series classification algorithms used as model selection methods for time series anomaly detection. Overall, our system enables users to compare 17 different classifiers over 1980 time series, and decide on the most suitable time series classification method for their own time series and use cases.
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Dates and versions

hal-04590326 , version 1 (28-05-2024)

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  • HAL Id : hal-04590326 , version 1

Cite

Paul Boniol, Emmanouil Sylligardos, John Paparrizos, Panos Trahanias, Themis Palpanas. ADecimo: Model Selection for Time Series Anomaly Detection. ICDE 2024 - IEEE 40th International Conference on Data Engineering, May 2024, Utrecht, Netherlands. ⟨hal-04590326⟩
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