{"user_id":"68554","date_created":"2019-12-03T13:49:19Z","date_updated":"2023-03-15T13:49:38Z","title":"Shift-Invariant Feature Extraction for Time-Series Motif Discovery","language":[{"iso":"eng"}],"department":[{"_id":"DEP5023"}],"place":"Dortmund","doi":"10.5445/KSP/1000049620","page":"23-45","type":"conference","citation":{"chicago-de":"Deppe, Sahar und Volker Lohweg. 2015. Shift-Invariant Feature Extraction for Time-Series Motif Discovery. In: <i>25. Workshop Computational Intelligence VDI/VDE-Gesellschaft Mess- und Automatisierungstechnik (GMA)</i>, 23–45. Dortmund. doi:<a href=\"https://doi.org/10.5445/KSP/1000049620,\">10.5445/KSP/1000049620,</a> .","din1505-2-1":"<span style=\"font-variant:small-caps;\">Deppe, Sahar</span> ; <span style=\"font-variant:small-caps;\">Lohweg, Volker</span>: Shift-Invariant Feature Extraction for Time-Series Motif Discovery. In: <i>25. Workshop Computational Intelligence VDI/VDE-Gesellschaft Mess- und Automatisierungstechnik (GMA)</i>. Dortmund, 2015, S. 23–45","ama":"Deppe S, Lohweg V. Shift-Invariant Feature Extraction for Time-Series Motif Discovery. In: <i>25. Workshop Computational Intelligence VDI/VDE-Gesellschaft Mess- Und Automatisierungstechnik (GMA)</i>. Dortmund; 2015:23-45. doi:<a href=\"https://doi.org/10.5445/KSP/1000049620\">10.5445/KSP/1000049620</a>","van":"Deppe S, Lohweg V. Shift-Invariant Feature Extraction for Time-Series Motif Discovery. In: 25 Workshop Computational Intelligence VDI/VDE-Gesellschaft Mess- und Automatisierungstechnik (GMA). Dortmund; 2015. p. 23–45.","mla":"Deppe, Sahar, and Volker Lohweg. “Shift-Invariant Feature Extraction for Time-Series Motif Discovery.” <i>25. Workshop Computational Intelligence VDI/VDE-Gesellschaft Mess- Und Automatisierungstechnik (GMA)</i>, 2015, pp. 23–45, doi:<a href=\"https://doi.org/10.5445/KSP/1000049620\">10.5445/KSP/1000049620</a>.","ieee":"S. Deppe and V. Lohweg, “Shift-Invariant Feature Extraction for Time-Series Motif Discovery,” in <i>25. Workshop Computational Intelligence VDI/VDE-Gesellschaft Mess- und Automatisierungstechnik (GMA)</i>, 2015, pp. 23–45.","havard":"S. Deppe, V. Lohweg, Shift-Invariant Feature Extraction for Time-Series Motif Discovery, in: 25. Workshop Computational Intelligence VDI/VDE-Gesellschaft Mess- Und Automatisierungstechnik (GMA), Dortmund, 2015: pp. 23–45.","bjps":"<b>Deppe S and Lohweg V</b> (2015) Shift-Invariant Feature Extraction for Time-Series Motif Discovery. <i>25. Workshop Computational Intelligence VDI/VDE-Gesellschaft Mess- Und Automatisierungstechnik (GMA)</i>. Dortmund, pp. 23–45.","ufg":"<b>Deppe, Sahar/Lohweg, Volker (2015)</b>: Shift-Invariant Feature Extraction for Time-Series Motif Discovery, in: <i>25. Workshop Computational Intelligence VDI/VDE-Gesellschaft Mess- und Automatisierungstechnik (GMA)</i>, Dortmund, S. 23–45.","apa":"Deppe, S., &#38; Lohweg, V. (2015). Shift-Invariant Feature Extraction for Time-Series Motif Discovery. In <i>25. Workshop Computational Intelligence VDI/VDE-Gesellschaft Mess- und Automatisierungstechnik (GMA)</i> (pp. 23–45). Dortmund. <a href=\"https://doi.org/10.5445/KSP/1000049620\">https://doi.org/10.5445/KSP/1000049620</a>","chicago":"Deppe, Sahar, and Volker Lohweg. “Shift-Invariant Feature Extraction for Time-Series Motif Discovery.” In <i>25. Workshop Computational Intelligence VDI/VDE-Gesellschaft Mess- Und Automatisierungstechnik (GMA)</i>, 23–45. Dortmund, 2015. <a href=\"https://doi.org/10.5445/KSP/1000049620\">https://doi.org/10.5445/KSP/1000049620</a>.","short":"S. Deppe, V. Lohweg, in: 25. Workshop Computational Intelligence VDI/VDE-Gesellschaft Mess- Und Automatisierungstechnik (GMA), Dortmund, 2015, pp. 23–45."},"author":[{"full_name":"Deppe, Sahar","id":"52121","first_name":"Sahar","last_name":"Deppe"},{"last_name":"Lohweg","first_name":"Volker","id":"1804","full_name":"Lohweg, Volker","orcid":"0000-0002-3325-7887"}],"status":"public","main_file_link":[{"url":"https://publikationen.bibliothek.kit.edu/1000049620","open_access":"1"}],"publication":"25. Workshop Computational Intelligence VDI/VDE-Gesellschaft Mess- und Automatisierungstechnik (GMA)","abstract":[{"lang":"eng","text":"The means of data mining and machine learning tasks are important topics in signal processing fundamentals. An example of such tasks is motif discovery. This paper presents an efficient method for shift-invariant feature\r\nextraction in time-series motif discovery. The proposed method initiates from the machine learning procedure and tackles the drawbacks of existing methods. Moreover, the efficacy of the novel approach is benchmarked\r\nagainst various algorithms and data from diverse fields.\r\n"}],"oa":"1","year":2015,"_id":"2123"}