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Proceedings Paper

Ensemble encoding for time series forecasting with MLP networks
Author(s): Naveen Aerrabotu; Gene A. Tagliarini; Edward W. Page
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Paper Abstract

Neural networks represent a promising approach to time series forecasting; however, the problem of obtaining good network generalization continues to present a challenge. As a means of improving network generalization ability for time series forecasting applications, this paper investigates the utility of a biologically inspired scheme that employs receptive fields for encoding network inputs. Both single- and multi-step forecasting performances are studied in the context of the sunspot series. Additionally, a heuristic for selecting the placement and dilations of the receptive field functions is presented. The performance of multi-layered perceptron networks trained using the data arising from the encoding scheme is assessed. The heuristic for placing and dilating the receptive fields yielded networks that learn rapidly and have consistently good multi-step prediction capability as compared to other published results.

Paper Details

Date Published: 4 April 1997
PDF: 6 pages
Proc. SPIE 3077, Applications and Science of Artificial Neural Networks III, (4 April 1997); doi: 10.1117/12.271468
Show Author Affiliations
Naveen Aerrabotu, Clemson Univ. (United States)
Gene A. Tagliarini, Clemson Univ. (United States)
Edward W. Page, Clemson Univ. (United States)

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

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