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Abstract
Forecasting Bitcoin Price Utilizing Evolutionary Radial Bias Function Networks - Volume -2 | Issue - 1 | 2024, (JAN-MAR)

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.
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