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Proceeding Paper

Alone We Can Do So Little; Together We Cannot Be Detected †

1
Department of Computer Science, Heinrich Heine University, Universitätsstr. 1, 40225 Düsseldorf, Germany
2
Department of Financial Accounting & Auditing, Albert Ludwigs University Freiburg, Rempartstraße 10–16, 79098 Freiburg, Germany
*
Authors to whom correspondence should be addressed.
Presented at the 8th International Conference on Time Series and Forecasting, Gran Canaria, Spain, 27–30 June 2022.
These authors contributed equally to this work.
Academic Editors: Ignacio Rojas, Hector Pomares, Olga Valenzuela, Fernando Rojas and Luis Javier Herrera
Eng. Proc. 2022, 18(1), 3; https://doi.org/10.3390/engproc2022018003
Published: 17 June 2022
It is no longer possible to imagine our everyday life without time series data. This includes, for example, market developments, COVID-19 cases, electricity prices, and other data from a wide variety of domains. An important task in the analysis of these data is the detection of anomalies. In most cases, this is accomplished by examining individual time series. In our work, we use the techniques of cluster analysis to establish a relationship between time series and groups of time series. This relationship allows us to observe the development of time series in their entirety, thereby gaining additional insights. Our approach identifies outliers with a real-world reference and enables the user to locate outliers without prior knowledge. To underline the strengths of our approach, we compare our method with another known method on two real-world datasets. We found that our solution needs significantly fewer calculations, produces more reasonable results, and can be applied to real-time data. Moreover, our method detected additional outliers, whose occurrence could be explained by real events. View Full-Text
Keywords: outlier detection; outlier detection in time series; time series analysis; time series clustering; time series cluster evaluation outlier detection; outlier detection in time series; time series analysis; time series clustering; time series cluster evaluation
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MDPI and ACS Style

Korlakov, S.; Klassen, G.; Bravidor, M.; Conrad, S. Alone We Can Do So Little; Together We Cannot Be Detected. Eng. Proc. 2022, 18, 3. https://doi.org/10.3390/engproc2022018003

AMA Style

Korlakov S, Klassen G, Bravidor M, Conrad S. Alone We Can Do So Little; Together We Cannot Be Detected. Engineering Proceedings. 2022; 18(1):3. https://doi.org/10.3390/engproc2022018003

Chicago/Turabian Style

Korlakov, Sergej, Gerhard Klassen, Marcus Bravidor, and Stefan Conrad. 2022. "Alone We Can Do So Little; Together We Cannot Be Detected" Engineering Proceedings 18, no. 1: 3. https://doi.org/10.3390/engproc2022018003

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