ML Anthology

ADEPT: Anomaly Detection, Explanation and Processing for Time Series with a Focus on Energy Consumption Data

Benedikt Tobias Müller, Marvin Ender, Jan Erik Swiadek, Mengcheng Jin, Simon Winkel, Dominik Niedziela, Bin Li, Jelle Hüntelmann, Emmanuel Müller
ECML-PKDD 2022 pp. 622-626
doi:10.1007/978-3-031-26422-1_43 /ecmlpkdd/2022/muller2022ecmlpkdd-adept/

Abstract

Anomaly detection techniques are applicable for recognizing excessive energy consumption and device failure, thereby contributing to the maintenance of operational and sustainable energy supply systems. In this context, human decision makers can benefit from receiving explanation attempts for detected anomalies as part of a semi-automated software solution. Therefore we introduce the framework ADEPT, which comprises interfaces for processing user-supplied time series data and interactively visualizing explanatory anomaly information. Our framework features several shallow and deep machine learning algorithms for anomaly detection and explanation. We demonstrate ADEPT using energy consumption data collected from our university campus.

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Cite

Text

Müller et al. "ADEPT: Anomaly Detection, Explanation and Processing for Time Series with a Focus on Energy Consumption Data." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2022. doi:10.1007/978-3-031-26422-1_43

Markdown

[Müller et al. "ADEPT: Anomaly Detection, Explanation and Processing for Time Series with a Focus on Energy Consumption Data." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2022.](https://mlanthology.org/ecmlpkdd/2022/muller2022ecmlpkdd-adept/) doi:10.1007/978-3-031-26422-1_43

BibTeX

@inproceedings{muller2022ecmlpkdd-adept,
  title     = {{ADEPT: Anomaly Detection, Explanation and Processing for Time Series with a Focus on Energy Consumption Data}},
  author    = {Müller, Benedikt Tobias and Ender, Marvin and Swiadek, Jan Erik and Jin, Mengcheng and Winkel, Simon and Niedziela, Dominik and Li, Bin and Hüntelmann, Jelle and Müller, Emmanuel},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2022},
  pages     = {622-626},
  doi       = {10.1007/978-3-031-26422-1_43},
  url       = {https://mlanthology.org/ecmlpkdd/2022/muller2022ecmlpkdd-adept/}
}