Unsupervised Pattern Discovery in Automotive Time Series (Paperback)

Fabian Kai Dietrich Noering

ISBN 10: 3658363355 / ISBN 13: 9783658363352

Published by Springer Fachmedien Wiesbaden, Weisbaden, 2022

About this Item

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.

Seller Inventory # 9783658363352

Bibliographic Details

• Title: Unsupervised Pattern Discovery in Automotive Time Series (Paperback)
• Publisher: Springer Fachmedien Wiesbaden, Weisbaden
• Publication Date: 2022
• Language: English
• ISBN 10: 3658363355
• ISBN 13: 9783658363352
• Binding: Paperback
• Edition: 1st Edition
• Condition: new

About this title

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.

 

About the Author

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.