Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Videos

Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network.

Lukas Falat¹, Dusan Marcek², Maria Durisova¹

¹Faculty of Management Science and Informatics, University of Zilina, Univerzitna 8215/1, 010 26 Zilina, Slovakia.

Thescientificworldjournal

|March 16, 2016

Summary

Related Concept Videos

First Derivative Test: Problem Solving

First Derivative Test: Problem Solving

Imagine an asset price that crashes to a low point, rebounds sharply as bargain-hunters step in, and then gradually declines. Such behavior can be modeled with a smooth function whose turning points represent locally overvalued and undervalued regions. A convenient example that captures rebound followed by decay is:The high and low points of this curve are identified using the first derivative test, which determines where the function changes from increasing to decreasing or vice versa. To...

Newton’s Method

Newton’s Method

Newton’s Method is a powerful iterative technique for approximating the roots of real-valued, differentiable functions, particularly when analytical solutions are impractical. This approach is widely used in scientific computing, engineering, and finance, where equations may be too complex for traditional algebraic methods to handle. The method relies on an iterative process that refines an initial estimate using the function’s derivative to approach the true solution progressively.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Analysis of immune cell populations in atrial myocardium of patients with atrial fibrillation or sinus rhythm.

PloS one·2017

Same author

A physiological view and structures of mean residence times.

General physiology and biophysics·2013

Same author

Physiologically based structure of mean residence time.

TheScientificWorldJournal·2012

Same author

Novel approach to bioequivalence assessment based on physiologically motivated model.

International journal of pharmaceutics·2009

Same author

Computer controlled sequential simulation method: reconsidering evaluation of measurements from frequently sampled intravenous glucose tolerance test.

Computer methods and programs in biomedicine·2009

Same author

Physiologically motivated time-delay model to account for mechanisms underlying enterohepatic circulation of piroxicam in human beings.

Basic & clinical pharmacology & toxicology·2008

This study introduces a hybrid neural network for financial forecasting. The model combines a Radial Basis Function (RBF) neural network, genetic algorithm, and moving average to improve exchange rate predictions.

Area of Science:

Quantitative finance
Computational intelligence
Financial forecasting

Background:

Accurate prediction models are crucial for management decision-making in finance.
Existing models may lack the precision required for high-frequency financial data.
Soft computing offers advanced techniques for complex prediction tasks.

Purpose of the Study:

To develop and evaluate a novel hybrid neural network for financial time series prediction.
To enhance the accuracy of exchange rate forecasting using a combination of soft computing techniques.
To assess the model's performance against established forecasting methods.

Main Methods:

Development of a hybrid neural network integrating Radial Basis Function (RBF) network, genetic algorithm, and moving average.

Related Experiment Videos

Application of the model to high-frequency USD/CAD exchange rate time series data.

Comparative analysis using out-of-sample testing against autoregressive models and standard neural networks.

Optimization of Artificial Neural Network (ANN) parameters using genetic algorithms, compared to backpropagation and K-means clustering.

Main Results:

The proposed hybrid neural network demonstrated superior accuracy in forecasting exchange rate values.
The model outperformed standard autoregressive and neural network models in one-day horizon predictions.
Genetic algorithm optimization proved effective in enhancing ANN parameter adaptation.
The hybrid model showed potential in reducing decision-making risks in financial management.

Conclusions:

The hybrid neural network offers a more accurate prediction tool for financial markets.
This approach effectively leverages soft computing for improved financial forecasting.
The model provides valuable insights for informed management decisions in volatile financial environments.