Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Discovery of Ill-Known Motifs in Time Series Data

Discovery of Ill-Known Motifs in Time Series Data

Deppe, Sahar

Paperback

Series: Technologien Für Die Intelligente Automation, Book 15

Technology & EngineeringGeneral ComputersProbability & Statistics

ISBN10: 366264214X
ISBN13: 9783662642146
Publisher: Springer Vieweg
Published: Oct 2 2021
Pages: 205
Weight: 0.79
Height: 0.46 Width: 6.69 Depth: 9.61
Language: English

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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