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Discovery of Ill–Known Motifs in Time Series Data

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Discovery of Ill–Known Motifs in Time Series Data/ by Sahar Deppe.
作者:	Deppe, Sahar.
面頁冊數:	XIV, 205 p. 48 illus., 30 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Time-series analysis. -
電子資源:	https://doi.org/10.1007/978-3-662-64215-3
ISBN:	9783662642153

Discovery of Ill–Known Motifs in Time Series Data
Deppe, Sahar.

Discovery of Ill–Known Motifs in Time Series Data[electronic resource] /by Sahar Deppe. - 1st ed. 2022. - XIV, 205 p. 48 illus., 30 illus. in color.online resource. - Technologien für die intelligente Automation, Technologies for Intelligent Automation,152522-8587 ;. - Technologien für die intelligente Automation, Technologies for Intelligent Automation,.

Introduction -- Preliminaries -- General Principles of Time Series Motif Discovery -- State of the Art in Time Series Motif Discovery -- Distortion-Invariant Motif Discovery -- Evaluation -- Conclusion and Outlook -- Appendices A-D.

This book includes a novel motif discovery for time series, KITE (ill-Known motIf discovery in Time sEries data), to identify ill-known motifs transformed by affine mappings such as translation, uniform scaling, reflection, stretch, and squeeze mappings. Additionally, such motifs may be covered with noise or have variable lengths. Besides KITE’s contribution to motif discovery, new avenues for the signal and image processing domains are explored and created. The core of KITE is an invariant representation method called Analytic Complex Quad Tree Wavelet Packet transform (ACQTWP). This wavelet transform applies to motif discovery as well as to several signal and image processing tasks. The efficiency of KITE is demonstrated with data sets from various domains and compared with state-of-the-art algorithms, where KITE yields the best outcomes. The Author Sahar Deppe studied Electrical Engineering and Information Technology at Halmstad University (Halmstad, Sweden) and the OWL University of Applied Sciences and Arts (Lemgo, Germany), where she received her Master degree. From 2013 to 2020 she was employed at the Institute Industrial IT (inIT) as a research associate and during this time she completed her doctorate (Dr. rer. nat.) in cooperative graduation with Paderborn University. Since 2020 she is employed at the Fraunhofer Institute IOSB-INA as a research associate with project management responsibilities. In her dissertation, she proposed a novel method to detect motifs in time series data based on mathematical theories suited to represent and handle ill-known motifs such as invariant theory and theories in signal processing such as wavelet theory. Her research interests include but are not limited to the area of motif discovery and time series analysis, pattern recognition, and machine learning. She has published and presented her research at numerous conferences and journals such as IEEE, IARIA, PESARO where she got the best paper award for her research in motif discovery in image data.

ISBN: 9783662642153

Standard No.: 10.1007/978-3-662-64215-3doiSubjects--Topical Terms:

528412
Time-series analysis.

LC Class. No.: QA280

Dewey Class. No.: 519.55

Discovery of Ill–Known Motifs in Time Series Data
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