Unsupervised Pattern Discovery in Automotive Time Series (Paperback)

Fabian Kai Dietrich Noering

ISBN 10: 3658363355 / ISBN 13: 9783658363352

Edité par Springer Fachmedien Wiesbaden, Weisbaden, 2022

A propos de cet article

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 multiple locations in the US or from the UK, depending on stock availability.

N° de réf. du vendeur 9783658363352

Détails bibliographiques

• Titre: Unsupervised Pattern Discovery in Automotive Time Series (Paperback)
• Éditeur: Springer Fachmedien Wiesbaden, Weisbaden
• Date de parution: 2022
• Langue: anglais
• ISBN à 10 chiffres: 3658363355
• ISBN à 13 chiffres: 9783658363352
• Reliure: Paperback
• Édition: Edition originale
• État de l'article: new

A propos de ce titre

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.

 

À propos de l?auteur

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.