TIME SERIES PREDICTION USING ICA ALGORITHMS | Zendy

J. M. Górriz | Zendy; Carlos G. Puntonet | Zendy; Moisés Salmerón | Zendy; Elmar W. Lang | Zendy

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TIME SERIES PREDICTION USING ICA ALGORITHMS

Author(s) -

J. M. Górriz,

Carlos G. Puntonet,

Moisés Salmerón,

Elmar W. Lang

Publication year - 2014

Publication title -

international journal of 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.

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