The aim of this work is to find individual and joint change-points in a large multivariate database of climate data. We model monthly values of precipitation, minimum and maximum temperature recorded in 360 stations covering all Italy for 60 years (12 × 60 months). The proposed three variate Gaussian change-point model exploits the Hierarchical Dirichlet process, allowing for a formalization that lets us estimate a different change-point model for each station. As stations possibly share some of the parameters of the trivariate normal emission distribution, this model framework provides an original definition of the change-points corresponding to changes in any subset of the 9 model parameters.
Multivariate change-point analysis for climate time series
Alessio Pollice;
2019-01-01
Abstract
The aim of this work is to find individual and joint change-points in a large multivariate database of climate data. We model monthly values of precipitation, minimum and maximum temperature recorded in 360 stations covering all Italy for 60 years (12 × 60 months). The proposed three variate Gaussian change-point model exploits the Hierarchical Dirichlet process, allowing for a formalization that lets us estimate a different change-point model for each station. As stations possibly share some of the parameters of the trivariate normal emission distribution, this model framework provides an original definition of the change-points corresponding to changes in any subset of the 9 model parameters.File | Dimensione | Formato | |
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