A Statistically Dependent Approach for the Monthly Rainfall Forecastfrom One Point Observations

In this work an adaptive linear filter model in a autoregressive moving average (ARMA) topology for forecasting time series is presented. The time series are composed by observations of the accumulative rainfall every month during several years. The learning rule used to adjust the filter coefficients is mainly based on the gradient-descendent method. In function of the long and short term stochastic dependence of the time series, we propose an on-line heuristic law to set the training process and to modify the filter topology. The input patterns for the predictor filter are the values of the time series after applying a time-delay operator. Hence, the filter’s output will tend to approximate the current value available from the data series. The approach is tested over a time series obtained from measures of the monthly accumulative rainfall from La Perla, Córdoba, Argentina. The performance of the presented approach is shown by forecasting the following 18 months from a hypothetical actual time for four time series of 102 data length.