Tandem Outlier Detectors for Decentralized Data

Author:

Heyden Marco1,Wilwer Jürgen1,Fouché Edouard1,Thoma Steffen2,Matthiesen Sven1,Gwosch Thomas1

Affiliation:

1. Karlsruhe Institute of Technology, Germany

2. FZI Research Center for Information Technology, Germany

Funder

German Federal Ministry of Education and Research (BMBF)

German Research Foundation (DFG)

Publisher

ACM

Reference23 articles.

1. M. Barandas , D. Folgado , 2020 . TSFEL: Time Series Feature Extraction Library. SoftwareX (2020). M. Barandas, D. Folgado, 2020. TSFEL: Time Series Feature Extraction Library. SoftwareX (2020).

2. Sourabh Bharti , K.   K. Pattanaik , and Anshul Pandey. 202 0. Contextual outlier detection for wireless sensor networks. JAIHC ( 2020 ). Sourabh Bharti, K. K. Pattanaik, and Anshul Pandey. 2020. Contextual outlier detection for wireless sensor networks. JAIHC (2020).

3. H. H. W. J. Bosman , G. Iacca, 2017. Spatial anomaly detection in sensor networks using neighborhood information. Inf. Fusion ( 2017 ). H. H. W. J. Bosman, G. Iacca, 2017. Spatial anomaly detection in sensor networks using neighborhood information. Inf. Fusion (2017).

4. M. Dörr J. Peters and S. Matthiesen. 2021. Data-Driven Analysis of Human-Machine Systems – A Data Logger and Possible Use Cases for Field Studies with Cordless Power Tools. In IHIET. M. Dörr J. Peters and S. Matthiesen. 2021. Data-Driven Analysis of Human-Machine Systems – A Data Logger and Possible Use Cases for Field Studies with Cordless Power Tools. In IHIET.

5. L. Fang and S. Dobson. 2014. Data Collection with In-network Fault Detection Based on Spatial Correlation. In ICCAC. L. Fang and S. Dobson. 2014. Data Collection with In-network Fault Detection Based on Spatial Correlation. In ICCAC.

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