NONLINEAR TIME SERIES PREDICTION BASED ON A POWER-LAW NOISE MODEL

Frank Emmert-Streib () and Matthias Dehmer ()
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Frank Emmert-Streib: Stowers Institute for Medical Research, 1000 E. 50th Street, Kansas City, MO 64110, USA
Matthias Dehmer: Discrete Mathematics and Geometry, Vienna University of Technology, Wiedner Hauptstrasse 8-10, A-1040 Vienna, Austria

International Journal of Modern Physics C (IJMPC), 2007, vol. 18, issue 12, 1839-1852

Abstract: In this paper we investigate the influence of a power-law noise model, also called Pareto noise, on the performance of a feed-forward neural network used to predict nonlinear time series. We introduce an optimization procedure that optimizes the parameters of the neural networks by maximizing the likelihood function based on the power-law noise model. We show that our optimization procedure minimizes themean squared errorleading to an optimal prediction. Further, we present numerical results applying our method to time series from the logistic map and the annual number of sunspots and demonstrate that a power-law noise model gives better results than a Gaussian noise model.

Keywords: Time series prediction; maximum likelihood; Monte Carlo method; feed-forward neural network (search for similar items in EconPapers)
Date: 2007
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DOI: 10.1142/S0129183107011765

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