Time Series Prediction of Bitcoin Cryptocurrency Price Based on Machine Learning Approach - Article

Aug 3, 2026

Bitcoin has attracted researchers and institutional investors due to its decentralized nature, high trading accessibility, and strong price volatility since 2009. Accurate price forecasting remains difficult, so a time-series prediction solution is built using machine learning models including SVR, KNN, XGBoost, and LSTM. The experiments use Bitcoin close prices from 2018 to 2023 and assess performance via R-squared, MAE, MSE, and RMSE, supported by dashboard visual comparisons between original and predicted values. LSTM achieves the highest accuracy; SVR ranks next, while XGBoost and KNN perform lower overall.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages15

Reading Time38 min

AccessFree to download

Introduction

Background and motivation

Research aims and contributions

Contents lists available online at TALENTA Publisher DATA SCIENCE: JOURNAL OF COMPUTING AND APPLIED INFORMATICS (JoCAI) Journal homepage: [https://talenta.usu.ac.id/JoCAI](https://talenta.usu.ac.id/JoCAI) | Time Series Prediction of Bitcoin Cryptocurrency Price Based on Machine Learning Approach
Eddie Ngai1, Salwani Abdullah2*, Mohd Zakree Ahmad Nazri3, Nor Samsiah Sani 4 and Zalinda Othman5
Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia
[email:](email:1 a180095@siswa.ukm.edu.my)[1](email:1 a180095@siswa.ukm.edu.my)[ a180095@siswa.ukm.edu.my](email:1 a180095@siswa.ukm.edu.my), [2](2 salwani@ukm.edu.my)[ salwani@ukm.edu.my](2 salwani@ukm.edu.my), [3](3 zakree@ukm.edu.my)[ zakree@ukm.edu.my](3 zakree@ukm.edu.my), [4](4 norsamsiahsani@ukm.edu.my)[ norsamsiahsani@ukm.edu.my](4 norsamsiahsani@ukm.edu.my), [5](5 zalinda@ukm.edu.my)[ zalinda@ukm.edu.my](5 zalinda@ukm.edu.my), | | | --- | --- | | A R T I C L E I N F O Article history:
Received 15 June 2023 Revised 22 June 2023
Accepted 17 July 2023
Published online 31 July 2023 Keywords:
Bitcoin Cryptocurrency Machine Learning
Corresponding Author:
[salwani@ukm.edu.my](salwani@ukm.edu.my) | A B S T R A C T
Over the past few years, Bitcoin has attracted the attention of numerous parties, ranging from academic researchers to institutional investors. Bitcoin is the first and most widely used cryptocurrency to date. Due to the significant volatility of the Bitcoin price and the fact that its trading method does not require a third party, it has gained great popularity since its inception in 2009 among a wide range of individuals. Given the previous difficulties in predicting the price of cryptocurrencies, this project will be developing and implementing a time series approach-based solution prediction model using machine learning algorithms which include Support Vector Machine Regression (SVR), K-Nearest Neighbor Regression (KNN), Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM) to determine the trend of bitcoin price movement, and assessing the effectiveness of the machine learning models. The data that will be used is the close prices of Bitcoin from the year 2018 up to the year 2023. The performance of the machine learning models is evaluated by comparing the results of R-squared, mean absolute error (MAE), mean squared error (RMSE), and also through a visualization graph of the original close price and predicted close price of Bitcoin in a dashboard. Among the models compared, LSTM emerged as the most accurate, followed by SVR, while XGBoost and KNN exhibited comparatively lower performance. | | IEEE style in citing this article:
E. Ngai, N.S. Sani, S. Abdullah, Z. Othman and M.Z.A. Nazri , "Time Series Prediction of Bitcoin Cryptocurrency Price Based on Machine Learning Approach," DATA SCIENCE: JOURNAL OF COMPUTING AND APPLIED INFORMATICS (JoCAI) , vol. 7, no. 2, pp. 81-95. 2023. | | 1. Introduction In 2009, Bitcoin was introduced to the public. Since then, it has become the most famous cryptocurrency in the world. According to [1], about 18 million Bitcoins (BTC) are sold and exchanged. Satoshi Nakamoto, the pseudonym of Bitcoin's developer, declared that Bitcoin's purpose was to function as a decentralized electronic payment system based on cryptographic evidence rather than trust [2] . High price volatility implies that certain steps need to be taken to accurately predict bitcoin prices [3]. Investors are usually concerned about asset price volatility because price changes result in immediate capital gains and losses. Given the volatility, it is always challenging to predict the bitcoin price. [4] found that accurate forecasting of bitcoin prices can provide decision support to investors and provide reference to the government to enact regulatory policies. Accurately predicting Bitcoin price movements is challenging due to the volatile nature of the crypt

FAQs

Why is predicting Bitcoin cryptocurrency price challenging?

Bitcoin shows significant volatility, and the market’s price fluctuations make forecasting difficult. Inaccurate predictions can harm investors, businesses, and organizations.

Which machine learning models are used for Bitcoin time series prediction?

The study evaluates SVR, K-Nearest Neighbor regression (KNN), XGBoost, and Long Short-Term Memory (LSTM) to model Bitcoin price trends.

How is model performance evaluated in the study?

Performance is measured by comparing results using R-squared, MAE, MSE, and RMSE, alongside visualization of original versus predicted close prices in a dashboard.

Aug 3, 2026

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