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Machine Learning Techniques for Time Series Classification

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Machine Learning Techniques for Time Series Classification (Volume 2) (English shop)

Michael Botsch (Author)


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Table of Contents, PDF (51 KB)

ISBN-13 (Hard Copy) 9783736978133
ISBN-13 (eBook) 9783736968134
Language English
Page Number 216
Lamination of Cover matt
Edition 2.
Book Series Künstliche Intelligenz & Digitalisierung
Volume 2
Publication Place Göttingen
Place of Dissertation TU München
Publication Date 2023-06-23
General Categorization Dissertation
Departments Electrical engineering
Keywords time series, car crashes, machine learning, airbags, protection of passengers

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.