TIME SERIES PREDICTION USING ICA ALGORITHMS

Authors

  • Juan M. Gorriz
  • Carlos G. Puntonet
  • Moises Salmeron
  • E. W. Lang

DOI:

https://doi.org/10.47839/ijc.2.2.208

Keywords:

Independent Component Analysis (ICA), Time Series Analysis, Neural Networks, Signal Processing

Abstract

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.

References

C.G. Puntonet A. Mansour, N. Ohnishi. Blind multiuser separation of instantaneous mixture algorithm based on geometrical concepts, Signal Processing 82 (2002). pp. 1155–1175.

M. Rodrguez Alvarez, C.G. Puntonet, I. Rojas. Separation of sources based on the partitioning of the space of observations, Lecture Notes in Computer Science 2085 (2001). pp. 762–769.

S. Amari, A. Cichocki, H. Yang. A new learning algorithm for blind source separation, Advances in Neural Information Processing Systems, MIT Press 8 (1996). pp. 757–763.

A. D. Back, A. S. Weigend. Discovering structure in finance using independent component analysis, Computational Finance (1997).

Andrew D. Back, Thomas P. Trappenberg. Selecting inputs for modelling using normalized higher order statistics and independent component analysis, IEEE Transactions on Neural Networks 12 (2001).

Andrew D. Back, A. S. Weigend. Discovering structure in finance using independent component analysis, 5th Computational Finance 1997 (1997).

A. J. Bell, T. J. Sejnowski. An information maximization approach to blind separation and blind deconvolution, Neural Computation 7 (1995), pp. 1129 – 1159.

G.E.P. Box, G.M. Jenkins, G.C. Reinsel. Time series analysis. forecasting and control. Prentice Hall, 1994.

P. Comon. Independent component analysis: A new concept?, Signal Processing 36 (1994). pp. 287–314.

S. Cruces. An unified view of bss algorithms(in spanish). University of Vigo, Spain, 1999.

J.M. Gorriz, J.J.G. dela Rosa, Carlos G. Puntonet, M. Salmeron Campos, New model for time-series forecasting using rbf?s and exogenous data, In Press (2003).

R.W. Hamming. Digital filters, 2a ed., Prentice Hall, 1983.

T. Hastie, R. Tibshirani, J. Friedman, The elements of statistical learning, Springer, 2000.

A. Hyvarinen, E. Oja. Independent component analysis: Algorithms and applications, Neural Networks 13 (2000). pp. 411–430.

A. Hyvrinen, E. Oja. Independent component analysis: algorithms and applications, Neural Networks 1 (2000). pp. 411–430.

K. Kiviluoto, E. Oja, Independent component analysis for paralell financial time series. Proc. in ICONIP98 1 (1998), pp. 895–898.

T. Masters. Neural, novel and hybrid algorithms for time series analysis prediction. John Miley & Sons, 1995.

J. Platt. A resource-allocating network for function interpolation, Neural Computation 3 (1991). pp. 213–225.

D.S.G. Pollock. A handbook of time series analysis, signal processing and dynamics. Academic Press, 1999.

W. H. Press, S. A. Teukolsky, W. T. Vertterling, B. P. Flannery. Numerical recipes in c++, 2a ed. Cambridge University Press, 2002.

C.G. Puntonet. Nuevos Algoritmos de Separacin de Fuentes en Medios Lineales. Ph.D. thesis, University of Granada, Departamento de Arquitectura y Tecnologia de Computadores, 1994.

C.G. Puntonet, Ali Mansour. Blind separation of sources using density estimation and simulated annealing, IEICE Transactions on Fundamental of Electronics Communications and Computer Sciences E84-A (2001).

J. M. Gorriz Saez. Prediccion y Tecnicas de Separacion de Senales. Ph.D. thesis, University of Cadiz, Departamento de Ing. de Sistemas y Aut. Tec. Eleectronica y Electronica, 2003.

M. Salmern-Campos. Prediccin de Series Temporales con Redes Neuronales de Funciones Radiales y Tecnicas de Descomposicion Matricial. Ph.D. thesis, University of Granada, Departamento de Arquitectura y Tecnologya de Computadores, 2001.

Moises Salmeron, Julio Ortega, Carlos G. Puntonet, Alberto Prieto. Improved ran sequential prediction using orthogonaltechniques, Neurocomputing 41 (2001). pp. 153–172.

A. Savitzky, M.J.E. Golay. Analytical Chemestry 36 (1964). pp. 1627–1639.

F. J Theis, A. Jung, E.W. Lang, C.G. Puntonet, Multiple recovery subspace projection algorithm employed in geometric ica, in press on Neural Computation (2001).

J. Moody, C. J. Darken. Fast learning in networks of locally-tuned processing units, Neural Computation 1 (1989). pp. 284–294.

H. Ziegler. Applied Spectroscopy 35 (1981). pp. 88-92.

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Published

2014-08-01

How to Cite

Gorriz, J. M., Puntonet, C. G., Salmeron, M., & Lang, E. W. (2014). TIME SERIES PREDICTION USING ICA ALGORITHMS. International Journal of Computing, 2(2), 69-75. https://doi.org/10.47839/ijc.2.2.208

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Section

Articles