Description

Book Synopsis

In the last decade unsupervised pattern discovery in time series, i.e. the problem of finding recurrent similar subsequences in long multivariate time series without the need of querying subsequences, has earned more and more attention in research and industry. Pattern discovery was already successfully applied to various areas like seismology, medicine, robotics or music. Until now an application to automotive time series has not been investigated. This dissertation fills this desideratum by studying the special characteristics of vehicle sensor logs and proposing an appropriate approach for pattern discovery. To prove the benefit of pattern discovery methods in automotive applications, the algorithm is applied to construct representative driving cycles.



Table of Contents
Introduction.- RelatedWork.- Development of Pattern Discovery Algorithms for Automotive Time Series.- Pattern-based Representative Cycles.- Evaluation.- Conclusion.

Unsupervised Pattern Discovery in Automotive Time

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    A Paperback / softback by Fabian Kai Dietrich Noering

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      View other formats and editions of Unsupervised Pattern Discovery in Automotive Time by Fabian Kai Dietrich Noering

      Publisher: Springer Fachmedien Wiesbaden
      Publication Date: 24/03/2022
      ISBN13: 9783658363352, 978-3658363352
      ISBN10: 3658363355
      Also in:
      Image processing Pattern recognition Automotive technology and trades

      Description

      Book Synopsis

      In the last decade unsupervised pattern discovery in time series, i.e. the problem of finding recurrent similar subsequences in long multivariate time series without the need of querying subsequences, has earned more and more attention in research and industry. Pattern discovery was already successfully applied to various areas like seismology, medicine, robotics or music. Until now an application to automotive time series has not been investigated. This dissertation fills this desideratum by studying the special characteristics of vehicle sensor logs and proposing an appropriate approach for pattern discovery. To prove the benefit of pattern discovery methods in automotive applications, the algorithm is applied to construct representative driving cycles.



      Table of Contents
      Introduction.- RelatedWork.- Development of Pattern Discovery Algorithms for Automotive Time Series.- Pattern-based Representative Cycles.- Evaluation.- Conclusion.

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