Unsupervised Pattern Discovery in Automotive Time Series
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Unsupervised Pattern Discovery in Automotive Time Series, Springer Vieweg
Pattern-based Construction of Representative Driving Cycles
Von Fabian Kai Dietrich Noering, im heise shop in digitaler Fassung erhältlich
Produktinformationen "Unsupervised Pattern Discovery in Automotive Time Series"
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.
Introduction.- RelatedWork.- Development of Pattern Discovery Algorithms for Automotive Time Series.- Pattern-based Representative Cycles.- Evaluation.- Conclusion.
Introduction.- RelatedWork.- Development of Pattern Discovery Algorithms for Automotive Time Series.- Pattern-based Representative Cycles.- Evaluation.- Conclusion.
Artikel-Details
- Anbieter:
- Springer Vieweg
- Autor:
- Fabian Kai Dietrich Noering
- Artikelnummer:
- 9783658363369
- Veröffentlicht:
- 23.03.22
- Seitenanzahl:
- 148