Forecasting Venezuelan Caroni river flow through support vector machines.
César Seijas, Sergio Villazana, Jorge Guevara, Edilberto Guevara Pérez · 2011
This paper models the time series formed by the monthly flows of Caroni River in Venezuela, since 1950 to 2003 using regression based on Support Vector Machines (SVR). The mentioned time series was preprocessed by extracting the trend and seasonal components through Independent Component Analysis (ICA) and the resulting stochastic series was modeled as a Nonlinear Autoregressive Moving Averaging process (NARMA), of order defined by Singular Value Decomposition (SVD), using SVR. The model was validated by obtaining a prediction error in the flow, lower than traditional statistical models. The results of this study demonstrate the strength of nonlinear computational models to predict river flows.