Unsupervised Pattern Discovery in Automotive Time Series (Paperback)

Fabian Kai Dietrich Noering

ISBN 10: 3658363355 / ISBN 13: 9783658363352

Editore: Springer Fachmedien Wiesbaden, Weisbaden, 2022

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Paperback. 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. In the last decade unsupervised pattern discovery in time series, i.e. To prove the benefit of pattern discovery methods in automotive applications, the algorithm is applied to construct representative driving cycles. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Codice articolo 9783658363352

Dati bibliografici

• Titolo: Unsupervised Pattern Discovery in Automotive Time Series (Paperback)
• Editore: Springer Fachmedien Wiesbaden, Weisbaden
• Data di pubblicazione: 2022
• Lingua: Inglese
• ISBN 10: 3658363355
• ISBN 13: 9783658363352
• Rilegatura: Paperback
• Edizione: prima edizione
• Condizione: new

Descrizione articolo

Riassunto

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.

 

Informazioni sull?autore

Fabian Kai Dietrich Noering is currently working in the technical development of Volkswagen AG as data scientist with a special interest in the analysis of time series regarding e.g. product optimization.