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

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

Ian T. Nabney, Alan McLachlan, David Lowe

Computer Science Research Group

Research output: Chapter in Book/Published conference output › Conference publication

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.

Original language	English
Title of host publication	Applications and science of artificial neural networks II
Editors	S.K. Rogers, D.W. Ruck
Publisher	SPIE
Pages	152-163
Number of pages	12
Volume	2760
DOIs	https://doi.org/10.1117/12.235906
Publication status	Published - 9 Apr 1996
Event	AeroSense '96 : Applications and Science of Artificial Neural Networks II - Orlando, United States Duration: 8 Apr 1996 → 12 Apr 1996

Publication series

Name	SPIE proceedings
Publisher	SPIE
Volume	2760
ISSN (Print)	0277-786X

Conference

Conference	AeroSense '96 : Applications and Science of Artificial Neural Networks II
Country/Territory	United States
City	Orlando
Period	8/04/96 → 12/04/96

Bibliographical note

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, 152 (March 22, 1996); doi:10.1117/12.235906.

Copyright 1996 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.

http://dx.doi.org/10.1117/12.235906

Keywords

RBF network
RAN
noise
plasticity
Kalman filter
training algorithm
tracking
real world non-stationary
complex adaptive models

Access to Document

10.1117/12.235906

Practical methods of tracking of non-stationary time series
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, 152 (March 22, 1996); doi:10.1117/12.235906. Copyright 1996 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. http://dx.doi.org/10.1117/12.235906
Final published version, 599 KB

Cite this

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title = "Practical methods of tracking of nonstationary time series applied to real-world data",

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.",

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Nabney, IT, McLachlan, A & Lowe, D 1996, Practical methods of tracking of nonstationary time series applied to real-world data. in SK Rogers & DW Ruck (eds), Applications and science of artificial neural networks II. vol. 2760, SPIE proceedings, vol. 2760, SPIE, pp. 152-163, AeroSense '96 : Applications and Science of Artificial Neural Networks II, Orlando, United States, 8/04/96. https://doi.org/10.1117/12.235906

Practical methods of tracking of nonstationary time series applied to real-world data. / Nabney, Ian T.; McLachlan, Alan; Lowe, David.
Applications and science of artificial neural networks II. ed. / S.K. Rogers; D.W. Ruck. Vol. 2760 SPIE, 1996. p. 152-163 (SPIE proceedings; Vol. 2760).

Research output: Chapter in Book/Published conference output › Conference publication

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N2 - 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.

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BT - Applications and science of artificial neural networks II

A2 - Rogers, S.K.

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PB - SPIE

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Y2 - 8 April 1996 through 12 April 1996

ER -

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

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

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Cite this