Time Series Chaotic Neural Oscillatory Networks for Financial Prediction

  • Raymond S. T. LeeEmail author


Financial prediction, ranging from stocks, commodities to forex forecast poses real challenges to researchers and quantitative analysts (a.k.a. quants) due to its highly chaotic and unpredictable nature. With the blooming of cryptocurrency trading which provides 24 × 7 online currency trading, the financial markets, especially the worldwide currency markets are more chaotic and fluctuating than before. An effective and reliable financial prediction system is profoundly required. With the integration of quantum price levels (QPL) evaluated by quantum finance technology, this chapter devises a Time Series Chaotic Neural Oscillatory Network (TSCNON) for worldwide financial market prediction to effectively resolve the system over-training and deadlock problems imposed by traditional recurrent neural networks using classical sigmoid-based activation functions. In terms of system implementation, TSCNON coalesces into 2048 trading-day time series financial data and 39 major financial signals as input signals for the real-time prediction of 129 worldwide financial products which includes: 9 major cryptocurrencies, 84 forex, 19 major commodities, and 17 worldwide financial indices. In terms of system performance, past 500-trading day of average system performance attained less than 1% forecast percentage errors.



The author wishes to thank and for the provision of historical and real-time financial data. The author also wishes to thank Quantum Finance Forecast Center of UIC for the R&D supports and the provision of the channel and platform for worldwide system testing and evaluation.


  1. Fausett, L. Fundamentals of Neural Networks Architectures Algorithms and Applications. Prentice Hall, 1994.Google Scholar
  2. Lee, R. S. T. LEE-Associator—A Transient Chaotic Autoassociative Network for Progressive Memory Recalling. Neural Networks. 19(5): 644–666, 2006a.CrossRefGoogle Scholar
  3. Lee, R. S. T. Fuzzy-Neuro Approach to Agent Applications (From the AI Perspective to Modern Ontology). Springer-Verlag, Germany, 2006b.Google Scholar
  4. Lee, R. S. T. Advanced Paradigms in Artificial Intelligence From Neural Oscillators, Chaos Theory to Chaotic Neural Networks. Advanced Knowledge International, Australia, 2005.Google Scholar
  5. Lee, R. S. T. A Transient-chaotic Auto-associative Network (TCAN) based on LEE-oscillators. IEEE Trans. Neural Networks. 15(5): 1228–1243, 2004.CrossRefGoogle Scholar
  6. Lee, R. S. T. (2019) Chaotic Type-2 Transient-Fuzzy Deep Neuro-Oscillatory Network (CT2TFDNN) for Worldwide Financial Prediction. IEEE Transactions on Fuzzy System.
  7. Li, G. C. L. and Lee, R. S. T. A Real-Time Scene Segmentation System Using Solely Excitatory Oscillator Networks (SEON). Journal of Intelligent Manufacturing. 16(6): 669–678, 2005.CrossRefGoogle Scholar
  8. Narayanan A. et al. Bitcoin and Cryptocurrency Technologies: A Comprehensive Introduction. Princeton University Press, 2016.Google Scholar
  9. Patterson, D. W. Artificial Neural Networks. Prentice Hall, 1996.Google Scholar
  10. Vigna, P. and Casey, M. J. The Age of Cryptocurrency: How Bitcoin and the Blockchain Are Challenging the Global Economic Order. Picador, 2016.Google Scholar
  11. Walker, W. Expert Advisor Programming and Advanced Forex Strategies. Independently published, 2018.Google Scholar
  12. Wong et al. Wind Shear Forecasting by Chaotic Oscillatory-based Neural Networks (CONN) with Lee-oscillator (Retrograde Signaling) Model. International Joint Conference on Neural Networks (IJCNN), 2040–2047, 2008.Google Scholar
  13. Young, A. R. Expert Advisor Programming for MetaTrader 4: Creating automated trading systems in the MQL4 language. Edgehill Publishing, 2015.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Division of Science and TechnologyBeijing Normal University-Hong Kong Baptist University United International College (UIC)ZhuhaiChina

Personalised recommendations