<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Due to the highly volatile tendency of Bitcoin, there is a necessity for a better price prediction model. Only a few researchers have focused on the feasibility to apply various modelling approaches. These approaches may prone to have low convergence issues in outcomes and acquire high computation time. Hence a model is put forward based on machine learning techniques using regression algorithm and Particle Swarm Optimization with XGBoost algorithm, for more precise prediction outcomes of three cryptocurrencies; Bitcoin, Dogecoin, and Ethereum. The approach uses time series that consists of daily price information of cryptocurrencies. In this paper, the XGBoost algorithm is incorporated with an enhanced PSO method to tune the optimal hyper-parameters to yield out better prediction output rate. The comparative assessment delineated that the proposed method shows less root mean squared error, mean absolute error and mean squared error values. In this aspect, the proposed model stands predominant in showing high efficiency of prediction rate.</p>
</abstract>

Due to the highly volatile tendency of Bitcoin, there is a necessity for a better price prediction model. Only a few researchers have focused on the feasibility to apply various modelling approaches. These approaches may prone to have low convergence issues in outcomes and acquire high computation time. Hence a model is put forward based on machine learning techniques using regression algorithm and Particle Swarm Optimization with XGBoost algorithm, for more precise prediction outcomes of three cryptocurrencies; Bitcoin, Dogecoin, and Ethereum. The approach uses time series that consists of daily price information of cryptocurrencies. In this paper, the XGBoost algorithm is incorporated with an enhanced PSO method to tune the optimal hyper-parameters to yield out better prediction output rate. The comparative assessment delineated that the proposed method shows less root mean squared error, mean absolute error and mean squared error values. In this aspect, the proposed model stands predominant in showing high efficiency of prediction rate.

Cryptocurrency Price Prediction Using Enhanced PSO with Extreme Gradient Boosting Algorithm

Department of Computer Science and Engineering

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Due to the highly volatile tendency of Bitcoin, there is a necessity for a better price prediction model. Only a few researchers have focused on the...

Cryptocurrency Price Prediction Using Enhanced PSO with Extreme Gradient Boosting Algorithm

Data publikacji: 12 cze 2023

Zakres stron: 170 - 187

Otrzymano: 14 gru 2022

Przyjęty: 19 maj 2023

DOI: https://doi.org/10.2478/cait-2023-0020

Słowa kluczowe
PSO, XGBoost, cryptocurrency, price prediction, Regression Algorithm

© 2023 Vibha Srivastava et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Cryptocurrency Price Prediction Using Enhanced PSO with Extreme Gradient Boosting Algorithm

Data publikacji: 12 cze 2023

Zakres stron: 170 - 187

Otrzymano: 14 gru 2022

Przyjęty: 19 maj 2023

DOI: https://doi.org/10.2478/cait-2023-0020

Słowa kluczowePSO, XGBoost, cryptocurrency, price prediction, Regression Algorithm

© 2023 Vibha Srivastava et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Słowa kluczowe
PSO, XGBoost, cryptocurrency, price prediction, Regression Algorithm