Modelling Time Series with Exogenous Information

J.M. Górriz, C.G. Puntonet, M. Salmerón, and J. Ortega (Spain)

Keywords

ICA algorithms, PCA, Artificial Neural Netwoks, SavitzkyGolay Filtering, Crossover Prediction Model

Abstract

In this paper we summarize various possible techniques such as Crossover Prediction Model or the classical Prin cipal Component Analysis (PCA) tool to include exoge nous data. Furthermore we propose a new method for volatile time series forecasting using Independent Compo nent Analysis (ICA) algorithms and Savitzky-Golay filter ing as preprocessing tools. The preprocessed data will be introduce in a based radial basis functions (RBF) Artificial Neural Network (ANN) and the prediction result will be compared with the one we get without these preprocessing tools.

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