Algoritmos híbridos para la modelización de series temporales con técnicas AR-ICA

Resumen

Over the last decades learning an input-output mapping from a set of simples using neural networks has been regarded such as performing approximation of multidimensional functions, regression or classification. In this process, a wide variety of different methods and approximations have been applied to many areas, such as medicine, business and finance, social sciences, and others. Them aim of this work is to generalize "on line" endogenous algorithms based on the empirical risk minimization principle in order to apply them to time series analysis and forecasting. We postulate a model based on admissible kernel functions and regularization theory following the philosophy of support vector machines, a new emerging choice for solving the problem of function approximation. The new algorithm called INAPA-PRED (Improved Neural model with Automatic Parameter Adjustment for PREDiction) is derived, and we demonstrate its capacity for yielding quality predictions that can be very useful in many areas. In addition we extend this method to exogenous time series, hubridizing with techniques such as Independent Component Analysis (ICA) or Genetic Algorithms (GA) for reducing the neural approximation error. Finally we prove the benefits when the proposed methods are applied to chaotic time series in the experimental sections. Mainly we have worked with series from finance and business although these models could be apply to many others.