Titlebook: Discovery of Ill–Known Motifs in Time Series Data; Sahar Deppe Book 2022 The Editor(s) (if applicable) and The Author(s), under exclusive

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2522-8579 variant and wavelet theory.Provides new directions for the d.This book includes a novel motif discovery for time series, KITE (.ill-Known motIf discovery in Time sE.ries data.), to identify ill-known motifs transformed by affine mappings such as translation, uniform scaling, reflection, stretch, and

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R.C. Dalton,C. Hölscher,H.J. Spiers and extract knowledge from it is growing. This issue is addressed by tasks such as clustering, classification, query by content, anomaly detection, and motif discovery [BeR14,BLB.17,FaV17,AAJ.19,AlA20].

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Book 2022avelet 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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Introduction,mount of data. According to IBM [IBM20], over 2.5 quintillion bytes of data are created every single day, obtained from different fields such as economics, medicine and epidemiology, industry and telecommunications, geographical and physical science [PfL18, ElB18, CTC.19, SGO20]. These data are main