Journal of Trends in Computer Science and Smart Technology is accepted for inclusion in Scopus. click here
Home / Archives / Volume-6 / Issue-2 / Article-5

Volume - 6 | Issue - 2 | june 2024

An Ensemble Machine Learning Technique for Bitcoin Price Prediction Open Access
S. Saraswathi  , Sridhala J S, A. Elavazhagan, Jasbir Singh Sabharwal, Sajid Ibni Mohammad  389
Pages: 153-167
Cite this article
Saraswathi, S., Sridhala J S, A. Elavazhagan, Jasbir Singh Sabharwal, and Sajid Ibni Mohammad. "An Ensemble Machine Learning Technique for Bitcoin Price Prediction." Journal of Trends in Computer Science and Smart Technology 6, no. 2 (2024): 153-167
Published
30 May, 2024
Abstract

This research proposes an ensemble approach for Bitcoin price prediction, leveraging historical price data and sentiment analysis. The proposed ensemble approach combines the model with Gated Recurrent Unit (GRU) and Bidirectional Long Short-Term Memory (BiLSTM) to further improve the accuracy in prediction by considering dynamics in the market. The model also addresses the problem of generalization and overfitting, adaption to the changing, dynamic nature of the market. Historical price data and sentiment scores from the preprocessing of the text are combined to the ensemble framework. These data are then fed into GRU and BiLSTM models for training, as the data contain not only complex temporal patterns but also sentiment-driven trends. The ensemble strategy could be beneficial for the strengths of the models and for improving the performances of the predictors. Most importantly, features are engineered in terms of technical indicators, lagged variables, and external factors impacting the price of Bitcoin. Sentiment analysis with the news and on social media complements insight into market sentiment, which adds value to the prediction power of the model.

Keywords

Bitcoin Sentiment Ensemble Prediction

×
Article Processing Charges

Journal of Trends in Computer Science and Smart Technology (jtcsst) is an open access journal. When a paper is accepted for publication, authors are required to pay Article Processing Charges (APCs) to cover its editorial and production costs. The APC for each submission is 400 USD. There are no additional charges based on color, length, figures, or other elements.

Category Fee
Article Access Charge 30 USD
Article Processing Charge 400 USD
Annual Subscription Fee 200 USD
Payment Gateway
Paypal: click here
Townscript: click here
Razorpay: click here
After payment,
please send an email to irojournals.contact@gmail.com / journals@iroglobal.com requesting article access.
Subscription form: click here