Highlights

  • ISSN Number : 2584-0673

  • Four Issues Per Year

  • No Publication Fee 

  • Free and Open Access Peer Reviewed Journal  

  • Articles Invited in English only 

  • Submission to a Final Decision: 45 Days

  • Acceptance to Publication: 12 Days

Track manuscript
Enter only the numeric value. (eg. Enter 2014000001, not IJIMSR2014000001) and click on Track
Abstract
Forecasting Bitcoin Price Utilizing Evolutionary Radial Bias Function Networks - Volume -2 | Issue - 1 | 2024, (MAR-MAY)

Pages:46-55

Category: Engineering & Technology

Published Date: 15-May-2024

Graphical Abstract

Sudersan Behera, P M Suresh

Keywords:

Time series forecasting, radial bias function network, Fireworks Algorithm

Abstract:
A semi-parametric evolutionary method called the Fireworks method (FWA) and a radial bias function neural network (RBFN) are combined in this study to make a new type of network called RBFN-FWA. Effectively adjusting the RBFN's biases and weights is the goal of integrating FWA. We next used this proposed methodology to predict how Bitcoin's price will go in the future. The solo RBFN was trained using various optimization techniques such as GA, PSO, and GD for comparison purposes. This resulted in three new models: RBFN-GA, RBFN-PSO, and RBFN-GD. We also use all the other models that can do the same thing. Utilizing error metrics like Mean Absolute Percentage Error (MAPE) and Normalized Mean Squared Error (NMSE), we evaluated the models' performance. In terms of NMSE and MAPE, the experimental findings show that RBFNN-FWA is the best comparison model, demonstrating its higher predictive potential.A semi-parametric evolutionary method called the Fireworks method (FWA) and a radial bias function neural network (RBFN) are combined in this study to make a new type of network called RBFN-FWA. Effectively adjusting the RBFN's biases and weights is the goal of integrating FWA. We next used this proposed methodology to predict how Bitcoin's price will go in the future. The solo RBFN was trained using various optimization techniques such as GA, PSO, and GD for comparison purposes. This resulted in three new models: RBFN-GA, RBFN-PSO, and RBFN-GD. We also use all the other models that can do the same thing. Utilizing error metrics like Mean Absolute Percentage Error (MAPE) and Normalized Mean Squared Error (NMSE), we evaluated the models' performance. In terms of NMSE and MAPE, the experimental findings show that RBFNN-FWA is the best comparison model, demonstrating its higher predictive potential.
Announcements

Call for Paper

Next Issue

Last Date : 15th November 2024

Search Articles

Indexed and Abstracted in

11_images.png
13_o7.png
14_o4.png
16_Open_Access_logo_with_dark_text_for_contrast,_on_transparent_background.png
17_ORCID_full_logo.png