{"publisher":"IEEE","type":"conference","citation":{"ieee":"C. Lenz, C. Henke, and A. Trächtler, “A Methodical Approach to Hybrid Modelling for Contextual Anomaly Detection on Time-Series Data,” 2023, doi: <a href=\"https://doi.org/10.1109/indin51400.2023.10218108\">10.1109/indin51400.2023.10218108</a>.","bibtex":"@inproceedings{Lenz_Henke_Trächtler_2023, title={A Methodical Approach to Hybrid Modelling for Contextual Anomaly Detection on Time-Series Data}, DOI={<a href=\"https://doi.org/10.1109/indin51400.2023.10218108\">10.1109/indin51400.2023.10218108</a>}, booktitle={2023 IEEE 21st International Conference on Industrial Informatics (INDIN)}, publisher={IEEE}, author={Lenz, Cederic and Henke, Christian and Trächtler, Ansgar}, year={2023} }","ama":"Lenz C, Henke C, Trächtler A. A Methodical Approach to Hybrid Modelling for Contextual Anomaly Detection on Time-Series Data. In: <i>2023 IEEE 21st International Conference on Industrial Informatics (INDIN)</i>. IEEE; 2023. doi:<a href=\"https://doi.org/10.1109/indin51400.2023.10218108\">10.1109/indin51400.2023.10218108</a>","short":"C. Lenz, C. Henke, A. Trächtler, in: 2023 IEEE 21st International Conference on Industrial Informatics (INDIN), IEEE, 2023.","apa":"Lenz, C., Henke, C., &#38; Trächtler, A. (2023). A Methodical Approach to Hybrid Modelling for Contextual Anomaly Detection on Time-Series Data. <i>2023 IEEE 21st International Conference on Industrial Informatics (INDIN)</i>. <a href=\"https://doi.org/10.1109/indin51400.2023.10218108\">https://doi.org/10.1109/indin51400.2023.10218108</a>","chicago":"Lenz, Cederic, Christian Henke, and Ansgar Trächtler. “A Methodical Approach to Hybrid Modelling for Contextual Anomaly Detection on Time-Series Data.” In <i>2023 IEEE 21st International Conference on Industrial Informatics (INDIN)</i>. IEEE, 2023. <a href=\"https://doi.org/10.1109/indin51400.2023.10218108\">https://doi.org/10.1109/indin51400.2023.10218108</a>.","mla":"Lenz, Cederic, et al. “A Methodical Approach to Hybrid Modelling for Contextual Anomaly Detection on Time-Series Data.” <i>2023 IEEE 21st International Conference on Industrial Informatics (INDIN)</i>, IEEE, 2023, doi:<a href=\"https://doi.org/10.1109/indin51400.2023.10218108\">10.1109/indin51400.2023.10218108</a>."},"department":[{"_id":"153"},{"_id":"241"}],"date_created":"2023-10-31T13:49:23Z","doi":"10.1109/indin51400.2023.10218108","publication_status":"published","_id":"48570","status":"public","author":[{"first_name":"Cederic","full_name":"Lenz, Cederic","last_name":"Lenz"},{"last_name":"Henke","full_name":"Henke, Christian","first_name":"Christian"},{"id":"552","first_name":"Ansgar","last_name":"Trächtler","full_name":"Trächtler, Ansgar"}],"year":"2023","user_id":"41470","title":"A Methodical Approach to Hybrid Modelling for Contextual Anomaly Detection on Time-Series Data","publication":"2023 IEEE 21st International Conference on Industrial Informatics (INDIN)","language":[{"iso":"eng"}],"quality_controlled":"1","date_updated":"2023-10-31T13:50:43Z"}