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

Area of Science:

• Computational neuroscience
• Financial modeling
• Complex systems analysis

Background:

• Recurrent neural networks (RNNs) have seen renewed interest due to reservoir computing.
• Reservoir computing offers high-speed, low-cost computation for non-linear systems.
• Financial and economic modeling is challenging due to volatility and non-linear relationships.

Purpose of the Study:

• To apply reservoir computing, specifically echo-state networks, to model complex financial systems.
• To investigate the impact of time-delayed feedback on model performance.
• To enhance the accuracy and efficiency of financial forecasting.

Main Methods:

• Employed an echo-state network (ESN) approach within the reservoir computing framework.
• Utilized varying strengths of time-delayed feedback to optimize the model.
• Tested the model's robustness against fluctuating financial parameters and trends.

Main Results:

• The developed ESN model demonstrated robustness against system trends and parameter fluctuations.
• The model accurately forecasts long-term financial system behavior.
• High accuracy was achieved in regenerating financial system unknowns with limited future data.

Conclusions:

• Reservoir computing with echo-state networks provides a powerful tool for complex financial system modeling.
• The approach offers significant improvements in forecasting accuracy and robustness.
• The model serves as a reliable data source for long-term financial decision-making and policy formulation.