Neural networks for event detection from time series: a BP algorithm approach

Authors:
Dayong Gao;Y. Kinouchi;K. Ito
Affiliations:
Faculty of Engineering, The University of Tokushima, Japan;Faculty of Engineering, The University of Tokushima, Japan;Faculty of the Integrated Arts and Sciences, The University of Tokushima, Japan
Venue:
ICCS'03 Proceedings of the 2003 international conference on Computational science: PartII
Year:
2003

Citing 3
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Neural networks for event extraction from time series: a back propagation algorithm approach

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Neural networks for event extraction from time series: a back propagation algorithm approach

Future Generation Computer Systems

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Abstract

In this paper, a relatively new event detection method using neural networks is developed for financial time series. Such method can capture homeostatic dynamics of the system under the influence of exogenous event. The results show that financial time series include both predictable deterministic and unpredictable random components. Neural networks can identify the properties of homeostatic dynamics and model the dynamic relation between endogenous and exogenous variables in financial time series input-output system. We also investigate the impact of the number of model inputs and the number of hidden layer neurons on forecasting.