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A reservoir computing approach for forecasting and regenerating both dynamical and time-delay controlled financial system behavior

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  • Rajat Budhiraja
  • Manish Kumar
  • Mrinal K Das
  • Anil Singh Bafila
  • Sanjeev Singh

Abstract

Significant research in reservoir computing over the past two decades has revived interest in recurrent neural networks. Owing to its ingrained capability of performing high-speed and low-cost computations this has become a panacea for multi-variate complex systems having non-linearity within their relationships. Modelling economic and financial trends has always been a challenging task owing to their volatile nature and no linear dependence on associated influencers. Prior studies aimed at effectively forecasting such financial systems, but, always left a visible room for optimization in terms of cost, speed and modelling complexities. Our work employs a reservoir computing approach complying to echo-state network principles, along with varying strengths of time-delayed feedback to model a complex financial system. The derived model is demonstrated to act robustly towards influence of trends and other fluctuating parameters by effectively forecasting long-term system behavior. Moreover, it also re-generates the financial system unknowns with a high degree of accuracy when only limited future data is available, thereby, becoming a reliable feeder for any long-term decision making or policy formulations.

Suggested Citation

  • Rajat Budhiraja & Manish Kumar & Mrinal K Das & Anil Singh Bafila & Sanjeev Singh, 2021. "A reservoir computing approach for forecasting and regenerating both dynamical and time-delay controlled financial system behavior," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-24, February.
    • Handle: RePEc:plo:pone00:0246737
    DOI: 10.1371/journal.pone.0246737
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    References listed on IDEAS

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    1. Hsieh, David A, 1991. "Chaos and Nonlinear Dynamics: Application to Financial Markets," Journal of Finance, American Finance Association, vol. 46(5), pages 1839-1877, December.
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