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Assessing driving risk through unsupervised detection of anomalies in telematics time series data

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003  MAP
005  20260202102310.0
008  260130e20250512bel|||p |0|||b|eng d
040  ‎$a‎MAP‎$b‎spa‎$d‎MAP
084  ‎$a‎6
100  ‎$0‎MAPA20260001180‎$a‎ Weng Chan, Ian
24510‎$a‎Assessing driving risk through unsupervised detection of anomalies in telematics time series data‎$c‎Ian Weng Chan, Andrei L. Badescu and X. Sheldon Lin
520  ‎$a‎Vehicle telematics provides granular data for dynamic driving risk assessment, but current methods often rely on aggregated metrics and do not fully exploit the rich time-series structure of telematics data. In this paper, we introduce a flexible framework using continuous-time hidden Markov model to model and analyse trip-level telematics data
650 4‎$0‎MAPA20080602437‎$a‎Matemática del seguro
650 4‎$0‎MAPA20080603779‎$a‎Seguro de automóviles
650 4‎$0‎MAPA20080556730‎$a‎Telemática
650 4‎$0‎MAPA20080601522‎$a‎Evaluación de riesgos
650 4‎$0‎MAPA20080576783‎$a‎Modelo de Markov
650 4‎$0‎MAPA20080578848‎$a‎Análisis de datos
7001 ‎$0‎MAPA20210030147‎$a‎Badescu, Andrei L.
7001 ‎$0‎MAPA20170014539‎$a‎Sheldon Lin, X.
7102 ‎$0‎MAPA20100017661‎$a‎International Actuarial Association
7730 ‎$w‎MAP20077000420‎$g‎12/05/2025 Volume 55 Issue 2 - may 2025 , p. 205 - 241‎$x‎0515-0361‎$t‎Astin bulletin‎$d‎Belgium : ASTIN and AFIR Sections of the International Actuarial Association