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Recent advances in time-series class...

Gellér, Zoltán.

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Recent advances in time-series classification -- methodology and applications

Record Type:	Electronic resources : Monograph/item
Title/Author:	Recent advances in time-series classification -- methodology and applications/ by Zoltán Gellér ... [et al.].
other author:	Gellér, Zoltán.
Published:	Cham :Springer Nature Switzerland : : 2025.,
Description:	xiv, 327 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Introduction -- Time Series and Similarity Measures -- Time Series Classification -- The impact of global constraints on the accuracy of elastic similarity measures.
Contained By:	Springer Nature eBook
Subject:	Time-series analysis - Data processing. -
Online resource:	https://doi.org/10.1007/978-3-031-77527-7
ISBN:	9783031775277

Recent advances in time-series classification -- methodology and applications
Recent advances in time-series classification -- methodology and applications[electronic resource] /by Zoltán Gellér ... [et al.]. - Cham :Springer Nature Switzerland :2025. - xiv, 327 p. :ill. (some col.), digital ;24 cm. - Intelligent systems reference library,v. 2641868-4408 ;. - Intelligent systems reference library ;v. 264..

Introduction -- Time Series and Similarity Measures -- Time Series Classification -- The impact of global constraints on the accuracy of elastic similarity measures.

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.

ISBN: 9783031775277

Standard No.: 10.1007/978-3-031-77527-7doiSubjects--Topical Terms:

700459
Time-series analysis
--Data processing.

LC Class. No.: QA280

Dewey Class. No.: 519.55

Recent advances in time-series classification -- methodology and applications
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260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2025.
300 $a xiv, 327 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Intelligent systems reference library, $x 1868-4408 ; $v v. 264
505 0 $a Introduction -- Time Series and Similarity Measures -- Time Series Classification -- The impact of global constraints on the accuracy of elastic similarity measures.
520 $a 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.
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