Multivariate linear parametric models applied to daily rainfall time series
| Autor: | C. Tallerini, Francesco Serinaldi, Salvatore Grimaldi |
|---|---|
| Přispěvatelé: | EGU, Publication |
| Jazyk: | angličtina |
| Rok vydání: | 2005 |
| Předmět: |
Multivariate statistics
lcsh:Dynamic and structural geology lcsh:QE1-996.5 Autocorrelation Linear model General Medicine Moving-average model lcsh:Geology lcsh:QE500-639.5 Autoregressive model Moving average Parametric model Statistics [SDU.STU] Sciences of the Universe [physics]/Earth Sciences lcsh:Q Autoregressive integrated moving average lcsh:Science Mathematics |
| Zdroj: | Advances in Geosciences, Vol 2, Pp 87-92 (2005) |
| ISSN: | 1680-7359 |
| Popis: | The aim of this paper is to test the Multivariate Linear Parametric Models applied to daily rainfall series. These simple models allow to generate synthetic series preserving both the time correlation (autocorrelation) and the space correlation (crosscorrelation). To have synthetic daily series, in such a way realistic and usable, it is necessary the application of a corrective procedure, removing negative values and enforcing the no-rain probability. The following study compares some linear models each other and points out the roles of autoregressive (AR) and moving average (MA) components as well as parameter orders and mixed parameters. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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