MODELLING TIME SERIES WITH EXOGENOUS INFORMATION

摘要

In this paper we summarize various possible techniques such as Crossover Prediction Model or the classical Principal Component Analysis (PCA) tool to include exogenous data. Furthermore we propose a new method for volatile time series forecasting using Independent Component Analysis (ICA) algorithms and Savitzky-Golay filtering 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.