Practical methods of tracking of nonstationary time series applied to real-world data

Ian T. Nabney; Alan McLachlan; David Lowe

doi:10.1117/12.235906

22 March 1996 Practical methods of tracking of nonstationary time series applied to real-world data

Ian T. Nabney, Alan McLachlan, David Lowe

Proceedings Volume 2760, Applications and Science of Artificial Neural Networks II; (1996) https://doi.org/10.1117/12.235906
Event: Aerospace/Defense Sensing and Controls, 1996, Orlando, FL, United States

Abstract

In this paper, we discuss some practical implications for implementing adaptable network algorithms applied to non-stationary time series problems. Two real world data sets, containing electricity load demands and foreign exchange market prices, are used to test several different methods, ranging from linear models with fixed parameters, to non-linear models which adapt both parameters and model order on-line. Training with the extended Kalman filter, we demonstrate that the dynamic model-order increment procedure of the resource allocating RBF network (RAN) is highly sensitive to the parameters of the novelty criterion. We investigate the use of system noise for increasing the plasticity of the Kalman filter training algorithm, and discuss the consequences for on-line model order selection. The results of our experiments show that there are advantages to be gained in tracking real world non-stationary data through the use of more complex adaptive models.

Citation Download Citation

Ian T. Nabney, Alan McLachlan, and David Lowe "Practical methods of tracking of nonstationary time series applied to real-world data", Proc. SPIE 2760, Applications and Science of Artificial Neural Networks II, (22 March 1996); https://doi.org/10.1117/12.235906

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