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

Discovery of Ill–Known Motifs in Time Series Data: Technologien für die intelligente Automation, cartea 15

Autor Sahar Deppe
en Limba Engleză Paperback – 2 oct 2021
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.
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Specificații

ISBN-13: 9783662642146
ISBN-10: 366264214X
Pagini: 205
Ilustrații: XIV, 205 p. 48 illus., 30 illus. in color.
Dimensiuni: 168 x 240 mm
Greutate: 0.36 kg
Ediția:1st ed. 2022
Editura: Springer Berlin, Heidelberg
Colecția Springer Vieweg
Seria Technologien für die intelligente Automation

Locul publicării:Berlin, Heidelberg, Germany

Cuprins

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.

Recenzii

“The book under review provides one such vantage point, and anyone whose work involves finding patterns in large amounts of data should take heed. … For those well versed in the mathematics of harmonics and waves, the book should prove very useful in showing how these theories can be applied to data series. But even those who are not specialists in this area, such as myself, can still gain many ideas from this useful tome.” (Eugene Callahan, Computing Reviews, October 11, 2022)

Notă biografică

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.

Textul de pe ultima copertă

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.

Caracteristici

Delivers a comprehensive review of methods in motif discovery along with the research gaps in this domain Covers mathematical theories as invariant and wavelet theory Provides new directions for the domain of image processing