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Discovery of Ill-Known Motifs in Time Series Data

Discovery of Ill-Known Motifs in Time Series Data

Sahar Deppe

111,44 €
IVA incluido
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Editorial:
Springer Nature B.V.
Año de edición:
2021
Materia
Probabilidad y estadística
ISBN:
9783662642146
111,44 €
IVA incluido
Disponible

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Sobre IBD

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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Otros libros del autor

  • Discovery of Ill-Known Motifs in Time Series Data
    Sahar Deppe
    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,...
    Disponible

    48,18 €