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

Author

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  • Lei Zhang

    (University of Regina)

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.

Suggested Citation

  • Lei Zhang, 2018. "Artificial Neural Network Based Chaotic Generator Design for The Prediction of Financial Time Series," Proceedings of International Academic Conferences 6409417, International Institute of Social and Economic Sciences.
    • Handle: RePEc:sek:iacpro:6409417
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    File URL: https://iises.net/proceedings/35th-international-academic-conference-barcelona-spain/table-of-content/detail?cid=64&iid=055&rid=9417
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    More about this item

    Keywords

    Aritificial Neural Network (ANN); chaotic generator; financial time series; prediction; optimizaiton;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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