Artificial Neural Network Based Chaotic Generator Design for The Prediction of Financial Time Series

Lei Zhang ()
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Lei Zhang: University of Regina

No 6409417, Proceedings of International Academic Conferences from International Institute of Social and Economic Sciences

Abstract: series. The ANN architecture is usually designed and optimized based on trial and error using a given training data set. It is generally required to obtain big data for ANN training in order to achieve good training performance. Financial time series are subject to highly complex conditions of external inputs and their dynamic features can change fast and unpredictably. The aim of this research is to design an adaptive ANN architecture, which can be trained in real time with short time series for near future prediction. ANN based chaotic system generator is designed for the simulation and analysis of the dynamic features in financial time series.

Keywords: Aritificial Neural Network (ANN); chaotic generator; financial time series; prediction; optimizaiton (search for similar items in EconPapers)
JEL-codes: C45 C52 C61 (search for similar items in EconPapers)
Pages: 1 page
Date: 2018-06
New Economics Papers: this item is included in nep-big, nep-cmp and nep-ore
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Published in Proceedings of the Proceedings of the 35th International Academic Conference, Barcelona, Jun 2018, pages 163-163

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Persistent link: https://EconPapers.repec.org/RePEc:sek:iacpro:6409417

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