Rainfall Forecasting Using Sub sampling Nonparametric Methods

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 feed-forward neural networks ANNs. The main contribution is to divide the rainfall time series forecasting problem using non-parametric 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 ANN-based 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