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Predicting physical time series using dynamic ridge polynomial neural networks.

Dhiya Al-Jumeily¹, Rozaida Ghazali², Abir Hussain¹

¹Applied Computing Research Group, Liverpool John Moores University, Liverpool, Mersyside, United Kingdom.

|August 27, 2014

Summary

This summary is machine-generated.

This study introduces a novel Dynamic Ridge Polynomial Neural Network for predicting physical time series. The new model shows improved signal-to-noise ratios for complex data like the Lorenz attractor and sunspot numbers.

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Area of Science:

Time series analysis
Computational neuroscience
Astrophysics
Climatology

Background:

Forecasting natural phenomena is crucial across scientific disciplines.
Time series prediction is vital for applications in control systems, engineering, environmental science, and economics.
Accurate prediction requires understanding and modeling past system behavior to forecast future states.

Purpose of the Study:

To introduce and evaluate a novel Dynamic Ridge Polynomial Neural Network (DRPNN) for physical time series prediction.
To combine the strengths of higher-order neural networks and recurrent neural networks in a single architecture.
To assess the DRPNN's performance on diverse and complex physical time series data.

Main Methods:

Developed a Dynamic Ridge Polynomial Neural Network (DRPNN) architecture.
Applied the DRPNN to predict four distinct physical time series: Lorenz attractor, AE index, sunspot number, and heat wave temperature.
Benchmarked DRPNN performance against established higher-order and feedforward neural network techniques.

Main Results:

The DRPNN demonstrated significant improvements in signal-to-noise ratio (SNR).
Performance gains were observed across all tested physical time series.
The DRPNN outperformed several benchmarked higher-order and feedforward neural network models.

Conclusions:

The Dynamic Ridge Polynomial Neural Network is an effective architecture for physical time series prediction.
The DRPNN offers superior performance, particularly in enhancing signal clarity.
This novel approach provides a valuable tool for forecasting complex natural phenomena.