Discovery of Ill Known Motifs in Time Series Data

This book PDF is perfect for those who love Mathematics genre, written by Sahar Deppe and published by Springer Nature which was released on 01 October 2021 with total hardcover pages 205. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Discovery of Ill Known Motifs in Time Series Data books below.

Discovery of Ill   Known Motifs in Time Series Data
Author : Sahar Deppe
File Size : 44,5 Mb
Publisher : Springer Nature
Language : English
Release Date : 01 October 2021
ISBN : 9783662642153
Pages : 205 pages
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Discovery of Ill Known Motifs in Time Series Data by Sahar Deppe Book PDF Summary

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.

Discovery of Ill Known Motifs in Time Series Data

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

Get Book
Discovery of Ill   Known Motifs in Time Series Data

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’

Get Book
Discovery of Ill   Known Motifs in Time Series Data

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’

Get Book
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