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008			211001s2022 gw | s |||| 0|eng d
020			_a9783662642153
024	7		_a10.1007/978-3-662-64215-3 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA280 _b2022 EB
100	1		_aDeppe, Sahar _eautor _9687043
245	0	0	_aDiscovery of Ill-Known Motifs in Time Series Data _cby Sahar Deppe
250			_a1st edition 2022
264		1	_aBerlin, Heidelberg _bSpringer International Publishing _c2022
300			_a1 recurso en línea (XIV, 205 páginas) _b48 ilustraciones, 30 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aTechnologien für die intelligente Automation Technologies for Intelligent Automation _x2522-8587 _v15
505	0		_aIntroduction -- 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.
520			_aThis 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.
988			_aSpringer_Engineering_2022
650		7	_2embne _9141270 _aSeries temporales
776	0	8	_iPrinted edition: _z9783662642146
776	0	8	_iPrinted edition: _z9783662642160
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-662-64215-3 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2023 _dz _eu _zSI