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A Prediction Model Based on Gated Nonlinear Spiking Neural Systems.

Yujie Zhang1, Qian Yang1, Zhicai Liu1

  • 1School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China.

International Journal of Neural Systems
|April 13, 2023
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Summary

This study introduces gated nonlinear spiking neural P (GNSNP) systems and a GNSNP model for effective exchange rate forecasting. The novel ERF-GNSNP model outperforms 25 baseline methods on nine datasets.

Keywords:
Nonlinear spiking neural P systemsexchange rate forecastinggated nonlinear spiking neural P systemsprediction model

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Area of Science:

  • Computational Intelligence
  • Membrane Computing
  • Artificial Neural Networks

Background:

  • Nonlinear spiking neural P (NSNP) systems are neural-like models inspired by biological neurons.
  • These systems exhibit complex nonlinear dynamics due to their inherent structure.

Purpose of the Study:

  • Introduce a variant of NSNP systems: gated nonlinear spiking neural P (GNSNP) systems.
  • Develop and evaluate a recurrent-like GNSNP model for exchange rate forecasting.
  • Propose the ERF-GNSNP model, integrating GNSNP with a dense layer for multivariate time series analysis.

Main Methods:

  • Introduced GNSNP systems as a variant of NSNP systems.
  • Developed a recurrent-like GNSNP model.
  • Created the ERF-GNSNP prediction model by combining the GNSNP model with a dense layer for multivariate time series.
  • Evaluated the ERF-GNSNP model against 25 baseline models using nine exchange rate datasets.

Main Results:

  • The proposed ERF-GNSNP model demonstrated significant effectiveness in exchange rate forecasting.
  • The dense layer successfully captured correlations within multivariate time series data.
  • Comparative analysis confirmed the superiority of the ERF-GNSNP model over numerous baseline prediction models.

Conclusions:

  • GNSNP systems offer a promising framework for advanced computational modeling.
  • The ERF-GNSNP model represents an effective approach for complex financial time series prediction.
  • The study validates the practical applicability of GNSNP systems in real-world forecasting challenges.