Anomaly detection (AD) in numerical temporal data series is a prominent task in many domains, including the analysis of industrial equipment operation, the processing of IoT data streams, and the monitoring of appliance energy consumption. The life-cycle of an AD application with a Machine Learning (ML) approach requires data collection and preparation, algorithm design and selection, training, and evaluation. All these activities contain repetitive tasks which could be supported by tools. This paper describes ODIN AD, a framework assisting the life-cycle of AD applications in the phases of data preparation, prediction performance evaluation, and error diagnosis.

ODIN AD: a framework supporting the life-cycle of time series anomaly detection applications

Niccolò Zangrando;Piero Fraternali;Rocio Nahime Torres;Marco Petri;Nicolò Oreste Pinciroli Vago;Sergio Luis Herrera Gonzalez
2022-01-01

Abstract

Anomaly detection (AD) in numerical temporal data series is a prominent task in many domains, including the analysis of industrial equipment operation, the processing of IoT data streams, and the monitoring of appliance energy consumption. The life-cycle of an AD application with a Machine Learning (ML) approach requires data collection and preparation, algorithm design and selection, training, and evaluation. All these activities contain repetitive tasks which could be supported by tools. This paper describes ODIN AD, a framework assisting the life-cycle of AD applications in the phases of data preparation, prediction performance evaluation, and error diagnosis.
2022
Proceedings of the 7th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases Workshop
978-3-031-24377-6
Time series; Anomaly detection; Data annotation; Model evaluation; Evaluation metrics
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1231750
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