Cepeda Cuervo, Edilberto and Achcar, Jorge Alberto and Andrade, Marinho G. Seasonal hydrological and meteorological time series. Reporte técnico. Sin Definir. (No publicado)
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Resumen
Time series models are often used in hydrology and meteorology to model streamflows series in order to make forecasting and generate synthetic series which are inputs for the analysis of complex water resources systems. In this paper we introduce a new modeling approach for hydrologic and meteorological time series assuming a continuous distribution for the data, where both the conditional mean and conditional variance parameters are modeled. Bayesian methods using standard MCMC (Markov Chain Monte Carlo Methods) are used to simulate samples for the joint posterior distribution of interest. Two applications to real data set illustrate the proposed methodology, assuming that the observations come from a normal, a gamma or a beta distribution. A first example is given by a time series of monthly averages of natural streamflows, measured in the year period ranging from 1931 to 2010 in Furnas hydroelectric dam, Brazil. A second example is given with a time series of 313 air humidity data measured in a weather station of Rio Claro, a Brazilian city located in southeastern of Brazil. These applications motivate us to introduce new classes of models to analyze hydrological and meteorological time series.
Tipo de documento: | Documento de trabajo - Monograph (Reporte técnico) | |
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Palabras clave: | Hydrology time series data; Meteorological time series; Seasonal time series | |
Temática: | 3 Ciencias sociales / Social sciences > 33 Economía / Economics 5 Ciencias naturales y matemáticas / Science > 55 Ciencias de la tierra / Earth sciences & geology | |
Unidad administrativa: | Sede Bogotá > Facultad de Ciencias > Departamento de Estadística | |
Código ID: | 9364 | |
Enviado por : | Dr. Edilberto Cepeda Cuervo | |
Enviado el día : | 19 Julio 2013 16:02 | |
Ultima modificación: | 19 Julio 2013 16:02 | |
Ultima modificación: | 19 Julio 2013 16:02 | |
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