Unsupervised pattern discovery deals with the identification of similar recurring subsequences in time series without any knowledge regarding these patterns. The unsupervised discovery of patterns in time series has proven to be beneficial in many different research areas. This thesis investigated the application of pattern discovery in automotive use cases, which was considered as a gap in research previously. The handling of automotive data, or vehicle time series data, is especially challenging because it unites properties like high dynamism, diversity, dimensionality and large sizes.


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    Title :

    Conclusion


    Additional title:

    AutoUni – Schriftenreihe


    Contributors:


    Publication date :

    2022-03-24


    Size :

    5 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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