Preprocessing time series with ICA and savitzky-golay filtering.
J. M. Górriz, Carlos G. Puntonet, Moisés Salmerón · 2004
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.