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A Hidden Markov model for the analysis of cylindrical time series
- Publication Year :
- 2015
-
Abstract
- A new hidden Markov model is proposed for the analysis of cylindrical time series, i.e. bivariate time series of intensities and angles. It allows to segment cylindrical time series according to a finite number of regimes that represent the conditional distributions of the data under specific environmental conditions. The model parsimoniously accommodates for circular-linear correlation, multimodality, skewness and temporal autocorrelation. A computationally efficient Expectation-Maximization algorithm is described to estimate the parameters and a parametric bootstrap routine is provided to compute confidence intervals. These methods are illustrated on cylindrical time series of wave heights and directions.
Details
- Language :
- English
- Database :
- OpenAIRE
- Accession number :
- edsair.od......3668..6b0b3340aacf669e745b2b286f4d7c42