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Modelo con retraso y correlaciĆ³n cruzada para predecir intensidad de la lluvia a corto plazo
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Abstract
El uso de los sistemas de cĆ³mputo, permitido el mejor acondicionamiento de las predicciones que se realizan de los fenĆ³menos atmosfĆ©ricos. Sin embargo, aĆŗn este tipo de predicciones es un reto que se les presenta a la mayorĆa de los cientĆficos de las ciencias atmosfĆ©ricas. Este trabajo busca encontrar un modelo que sea Ć³ptimo y construir un algoritmo rĆ”pido y eficaz. La correcta adecuaciĆ³n de los factores que intervienen en la predicciĆ³n es otro problema; ya que por su accionar, Ć©ste se dispersa de manera sĆŗbita en periodos cortos y por tal razĆ³n, no es fĆ”cil conseguir una secuencia de datos que sean aplicables a distintos modelos de series temporales. En el trabajo se explora la posibilidad de utilizar modelos que son funcionales en fenĆ³menos parecidos. Los vectores de desplazamiento, es otro problema que se aborda en este trabajo. Para esto, se utilizan mĆ©todos estadĆsticos robustos y de fĆ”cil aplicaciĆ³n, para encontrar soluciĆ³n a este problema.
The use of computer systems has permitted a better conditioning of the predictions made on atmospheric phenomena. However, these types of predictions still present themselves as a risk to the majority of the scientists in the atmospheric sciences. This work seeks to find an optimal model and construct an algorithm that is fast and efficient. The proper adjustment of the factors that intervene in the prediction is another problem; because of its actions, it disperses suddenly and in short periods of time. For this reason, it is not easy to find a sequence of data that will be applicable to different models of temporal series. This work will explore the possibility of using models that are functional in similar phenomena. The displacement vectors are another problem that abroad this work. For this, we utilize statistic robust methods and of fast application, to find the solution to this problem.
The use of computer systems has permitted a better conditioning of the predictions made on atmospheric phenomena. However, these types of predictions still present themselves as a risk to the majority of the scientists in the atmospheric sciences. This work seeks to find an optimal model and construct an algorithm that is fast and efficient. The proper adjustment of the factors that intervene in the prediction is another problem; because of its actions, it disperses suddenly and in short periods of time. For this reason, it is not easy to find a sequence of data that will be applicable to different models of temporal series. This work will explore the possibility of using models that are functional in similar phenomena. The displacement vectors are another problem that abroad this work. For this, we utilize statistic robust methods and of fast application, to find the solution to this problem.
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Date
2012-08
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Keywords
Algorithm, Vectors, Statistics robust method