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Machine Learning Techniques for Time Series Classification
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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.
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