Machine Learning Techniques for Time Series Classification

Michael Botsch

ISBN 10: 3736978138 / ISBN 13: 9783736978133

Published by Cuvillier, Cuvillier Jun 2023, 2023

About this Item

This item is printed on demand - Print on Demand Titel. Neuware -Classification of time series is an important task in various fields, e.g., medicine, finance, and industrial applications. This work discusses strong temporal classification using machine learning techniques. Here, two problems must be solved: the detection of those time instances when the class labels change and the correct assignment of the labels. For this purpose the scenario-based random forest algorithm and a segment and label approach are introduced. The latter is realized with either the augmented dynamic time warping similarity measure or with interpretable generalized radial basis function classifiers.The main application presented in this work is the detection and categorization of car crashes using machine learning. Depending on the crash severity different safety systems, e.g., belt tensioners or airbags must be deployed at time instances when the best-possible protection of passengers is assured.Cuvillier Verlag, Nonnenstieg 8, 37075 Göttingen 216 pp. Englisch.

Seller Inventory # 9783736978133

Bibliographic Details

• Title: Machine Learning Techniques for Time Series Classification
• Publisher: Cuvillier, Cuvillier Jun 2023
• Publication Date: 2023
• Language: English
• ISBN 10: 3736978138
• ISBN 13: 9783736978133
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 210W x 148H x 12D millimeters 210 millimeters width by 148 millimeters height by 12 millimeters depth
• Weight: 286 grams

About this title

Synopsis

Classification of time series is an important task in various fields, e.g., medicine, finance, and industrial applications. This work discusses strong temporal classification using machine learning techniques. Here, two problems must be solved: the detection of those time instances when the class labels change and the correct assignment of the labels. For this purpose the scenario-based random forest algorithm and a segment and label approach are introduced. The latter is realized with either the augmented dynamic time warping similarity measure or with interpretable generalized radial basis function classifiers. The main application presented in this work is the detection and categorization of car crashes using machine learning. Depending on the crash severity different safety systems, e.g., belt tensioners or airbags must be deployed at time instances when the best-possible protection of passengers is assured.