MULTIVARIATE TIME SERIES MODELING IN A CONNECTIONIST APPROACH

D. R. Kulkarni () and J. C. Parikh
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D. R. Kulkarni: Physical Research Laboratory, Navrangpura, Ahmedabad 380 009, India
J. C. Parikh: Physical Research Laboratory, Navrangpura, Ahmedabad 380 009, India

International Journal of Modern Physics C (IJMPC), 2000, vol. 11, issue 01, 159-173

Abstract: Multivariate models in the framework of artificial neural network have been constructed for systems where time series data of several variables is known. The models have been tested using computer generated data for the Lorenz and Henon systems. They are found to be robust and give accurate short term predictions. Analysis of the models is able to throw some light on theoretical questions related to multivariate "embedding" and removal of redundancy in the embedding.

Keywords: Multivariate Time Series Modeling; Artificial Neural Network; State Space Reconstruction; Nonlinear Dynamics; Prediction (search for similar items in EconPapers)
Date: 2000
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DOI: 10.1142/S0129183100000146

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