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Proceedings Paper

Study of time series prediction under noisy environment
Author(s): A. S. Pandya; D. R. Kulkarni; J. C. Parikh
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Paper Abstract

A study of time series predictions has been conducted using artificial neural networks and several concepts of non- linear dynamics. The ability of the proposed approach for modelling the underlying dynamics of complex systems in the presence of noisy signals is demonstrated. In our approach the embedding theorem of non-linear dynamics provides a basis for determining the time delay ((tau) ) and the embedding dimension of the complex system. The embedded vectors are then extracted from the time series data by the method of delays. Experiments were conducted by introducing gaussian noise of varying degrees in the systems with known dynamics and the robustness of multistep predictions was studied. Various ANN architectures including backpropagation, backpropagation through time, recurrent networks, etc. were studied and their performances are compared. Several applications including sine series prediction and chaotic systems such as the Lorenz series are presented. For a deterministic system like the sine series prediction, it was observed that the neural network was able to extract salient features such as frequency, amplitude and phase in case of relatively low Signal to Noise Ratio (SNR). In case of the chaotic system, the ANN model extracts the dynamics for high SNR values. Realistic application to prediction of sunspot numbers is also discussed.

Paper Details

Date Published: 4 April 1997
PDF: 11 pages
Proc. SPIE 3077, Applications and Science of Artificial Neural Networks III, (4 April 1997); doi: 10.1117/12.271515
Show Author Affiliations
A. S. Pandya, Physical Research Lab. (India)
D. R. Kulkarni, Physical Research Lab. (India)
J. C. Parikh, Physical Research Lab. (India)

Published in SPIE Proceedings Vol. 3077:
Applications and Science of Artificial Neural Networks III
Steven K. Rogers, Editor(s)

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