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

Authors:
D. Gao;Y. Kinouchi;K. Ito;X. Zhao
Affiliations:
310, IT Building, Department of Information Technology, National University of Ireland, Galway, Ireland and Faculty of Engineering, University of Tokushima, 2-1 Minami Josanjima, Tokushima 770-850 ...;Faculty of Engineering, University of Tokushima, 2-1 Minami Josanjima, Tokushima 770-8506, Japan;Faculty of the Integrated Arts and Sciences, University of Tokushima, 1-1 Minami Josanjima, Tokushima 770-8506, Japan;Institut de Researches Cliniques de Montreal, 110 Avenue des Pins Ouest, Montreal, Que., Canada
Venue:
Future Generation Computer Systems
Year:
2005

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Abstract

This paper presents a relatively new event detection method using neural networks for time series analysis. 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 explore the signaling mechanisms that transfer information in such dynamic system and investigate the impact of the number of model inputs and the number of hidden layer neurons on financial analysis.