Pronóstico de lluvia usando métodos no paramétricos con submestreo
(Rainfall Forecasting Using Sub sampling Nonparametric Methods)
Julían Pucheta (jpucheta@efn.uncor.edu)^{1}, Cristian Rodríguez Rivero (crodriguezrivero@efn.uncor.edu)^{1}, Martín Herrera (martincitohache@gmail.com)^{2}, Carlos Salas (calberto.salas@gmail.com)^{2}, Victor Sauchelli (vsauch@com.uncor.edu)^{1}
^{1}Universidad Nacional de Córdoba ^{2}Universidad Nacional de Catamarca
This paper appears in: Revista IEEE América Latina
Publication Date: Feb. 2013
Volume: 11, Issue: 1
ISSN: 15480992
Abstract:
This article presents a comparison of two sub
sampling nonparametric methods for designing algorithms to
forecast time series from the cumulative monthly rainfall. Both
approaches are based on artificial feedforward neural networks
ANNs. The main contribution is to divide the rainfall time series
forecasting problem using nonparametric methods by subdivision
into stages of smoothing, so in this manner the time series
are smoothed in order to simplify the prediction problem. The
first case depicts an algorithm to forecast high roughness time
series that set the parameters of a nonlinear autoregressive model
NAR based on ANNs, which uses as a reference the Hurst
parameter associated to the time series. The second case, the
methodology consists of generating smoothing time series by
sampling the time series data, and each individual time series is
associated with a predictor filter. Thus, depending on the data,
others time series are obtained by sampling with an increasing
interval. For each one of the time series generated, a specific
ANNbased filter is adjusted, and each one generates a forecast
that is then averaged among other subsamples time series,
resulting so in a mix of predictor filters. The results are evaluated
on high roughness time series from the Mackey Glass Equation
MG and from cumulative monthly historical rainfall data from
one geographic location. The results are encouraging; deserve
study and investment in implementation effort for the
geographical locations of interest
Index Terms:
Rainfall, Hurst's parameter, time series forecast, subsampling
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