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Proceedings Paper

Iterative time series prediction and analysis by embedding and multiple time-scale decomposition networks
Author(s): Neep Hazarika; David Lowe
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Paper Abstract

In this work we describe a method of estimating and characterizing appropriate data and model complexity in the context of long term iterated time series forecasting using embeddings and multiple time-scale decomposition techniques. An embedding of a signal is obtained which decouples multiple time scale effects such as seasonality and trend. The complexity and stability of networks are estimated and the performance of long term iteration is examined. The performance of the technique is tested using the real world time series problems of electricity load forecasting, and financial futures contracts.

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: 10.1117/12.271470
Show Author Affiliations
Neep Hazarika, Aston Univ. (United Kingdom)
David Lowe, Aston Univ. (United Kingdom)


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

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