TY  - BOOK
AU  - Noering,Fabian Kai Dietrich
ED  - SpringerLink (Online service)
TI  - Unsupervised Pattern Discovery in Automotive Time Series: Pattern-based Construction of Representative Driving Cycles 
T2  - AutoUni – Schriftenreihe,
SN  - 9783658363369
AV  - TL152.5 
PY  - 2022///
CY  - Wiesbaden
PB  - Springer Fachmedien Wiesbaden, Imprint: Springer Vieweg
KW  - Automotive engineering
KW  - Image processing—Digital techniques
KW  - Computer vision
KW  - Pattern recognition systems
KW  - Computer science
KW  - Automotive Engineering
KW  - Computer Imaging, Vision, Pattern Recognition and Graphics
KW  - Automated Pattern Recognition
KW  - Theory and Algorithms for Application Domains
KW  - Motor vehicle driving -- Mathematical models
KW  - Time-series analysis
N1  - Introduction -- RelatedWork -- Development of Pattern Discovery Algorithms for Automotive Time Series -- Pattern-based Representative Cycles -- Evaluation -- Conclusion
N2  - 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
UR  - https://doi.org/10.1007/978-3-658-36336-9
ER  -