Open AccessTIME SERIES PREDICTION USING ICA ALGORITHMS
Author(s) -
Juan Manuel Górriz,
Carlos G. Puntonet,
Moisés Salmerón,
Elmar Lang
Publication year - 2014
Publication title -
computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.184
H-Index - 11
eISSN - 2312-5381
pISSN - 1727-6209
DOI - 10.47839/ijc.2.2.208
Subject(s) - independent component analysis , preprocessor , principal component analysis , computer science , artificial neural network , series (stratigraphy) , algorithm , time series , artificial intelligence , component (thermodynamics) , pattern recognition (psychology) , data pre processing , data mining , machine learning , paleontology , physics , biology , thermodynamics
In this paper 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 or the classical Principal Component Analysis (PCA) tool.