Unsupervised Pattern Discovery In Automotive Time Series Patternbased Construction Of Representative Driving Cycles Fabian Kai Dietrich Noering by Fabian Kai Dietrich Noering 9783658363352, 3658363355 instant download after payment.

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.