Discovery of Ill–Known Motifs in Time Series Data

Discover groundbreaking insights in "Discovery of Ill–Known Motifs in Time Series Data" by Sahar Deppe, published by Springer Fachmedien Wiesbaden in 2021. This first edition, spanning 205 pages, introduces the innovative KITE method—ill-Known motIf discovery in Time sEries data. KITE is designed to identify ill-known motifs that have been transformed through various affine mappings, including translation, uniform scaling, reflection, stretch, and squeeze mappings. This book is essential for researchers and practitioners in the field of time series analysis, providing a fresh perspective on motif discovery. Enhance your understanding of time series data with this comprehensive guide that combines theoretical insights with practical applications. Perfect for those looking to elevate their analytical skills and explore new dimensions in data interpretation.