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Recent Advances in Time-Series Classification—Methodology and Applications

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Springer


Paru le : 2025-04-26

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Description

This book examines the impact of such constraints on elastic time-series similarity measures and provides guidance on selecting suitable measures. Time-series classification frequently relies on selecting an appropriate similarity or distance measure to compare time series effectively, often using dynamic programming techniques for more robust results. However, these techniques can be computationally demanding, which results in the usage of global constraints to reduce the search area in the dynamic programming matrix. While these constraints cut computation time significantly (by up to three orders of magnitude), they may also affect classification accuracy.
Additionally, the importance of the nearest neighbor classifier (1NN) is emphasized for its strong performance in time-series classification, alongside the kNN classifier which offers stable results. This book further explores the weighted kNN classifier, which gives closer neighbors more influence, showing how it merges accuracy and stability for improved classification outcomes.
 
Pages
327 pages
Collection
n.c
Parution
2025-04-26
Marque
Springer
EAN papier
9783031775260
EAN PDF
9783031775277
Informations sur l'ebook
Nombre pages copiables
3
Nombre pages imprimables
32
Taille du fichier
26348 Ko
Prix
158,24 €
EAN EPUB
9783031775277
Informations sur l'ebook
Nombre pages copiables
3
Nombre pages imprimables
32
Taille du fichier
71086 Ko
Prix
158,24 €

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