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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Unsupervised Pattern Discovery in Automotive Time Series: Pattern-Based Construction of Representative Driving Cycles

Unsupervised Pattern Discovery in Automotive Time Series: Pattern-Based Construction of Representative Driving Cycles

Noering, Fabian Kai Dietrich

Paperback

Series: Autouni - Schriftenreihe, Book 159

Technology & EngineeringGeneral Computers

ISBN10: 3658363355
ISBN13: 9783658363352
Publisher: Springer Vieweg
Published: Mar 24 2022
Pages: 148
Weight: 0.47
Height: 0.37 Width: 5.83 Depth: 8.27
Language: English

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.

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