The Effects of Noise Reduction on the Prediction Accuracy of Time Series
Billings, S.A. and Lee, K.L.
(2002)
The Effects of Noise Reduction on the Prediction Accuracy of Time Series.
Research Report.
ACSE Research Report 817
.
Department of Automatic Control and Systems Engineering
Abstract
A new iterative smoothing method based on the extended Kalman filter is introduced to smooth noisy chaotic time series. Two examples are given to illustrate the smoothing method. The smoothing method is then employed as a noise prior to identification and prediction. Three different prediction methods are introduced and the prediction performance is compared using three nonlinear examples. Superior predictive performance is obtained by the prediction method that employs the pre-processing step on the data.
Metadata
Item Type:
Monograph
Authors/Creators:
Billings, S.A.
Lee, K.L.
Copyright, Publisher and Additional Information:
The Department of Automatic Control and Systems Engineering research reports offer a forum for the research output of the academic staff and research students of the Department at the University of Sheffield. Papers are reviewed for quality and presentation by a departmental editor. However, the contents and opinions expressed remain the responsibility of the authors. Some papers in the series may have been subsequently published elsewhere and you are advised to cite the later published version in these instances.
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