Advances in Economics, Management and Political Sciences

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Proceedings of the 2nd International Conference on Financial Technology and Business Analysis

Series Vol. 61 , 28 December 2023


Open Access | Article

Bitcoin Price Prediction: ARIMA & SARIMA vs Linear Regression

Junyi Zhu * 1
1 School of Social Sciences, University of Manchester, Manchester, The United Kingdom

* Author to whom correspondence should be addressed.

Advances in Economics, Management and Political Sciences, Vol. 61, 47-54
Published 28 December 2023. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Junyi Zhu. Bitcoin Price Prediction: ARIMA & SARIMA vs Linear Regression. AEMPS (2023) Vol. 61: 47-54. DOI: 10.54254/2754-1169/61/20230776.

Abstract

This paper illustrates the working process of predicting the Bitcoin price applying ARIMA, SARIMA and linear regression. Since more and more machine learning models were developed and tested in the financial field, these three models are selected to examine their reliabilities. In this study, three methodologies have been used for the Bitcoin predictions under the data set of Bitcoin historical prices. With the help of python notebook, order (1, 1, 1) and seasonal order (0, 1, 1, 12) were applied to the predictions in ARIMA and SARIMA respectively. In terms of linear regression, this paper used two independent variables including historical data and trading volume to predict the Bitcoin prices. It was discovered that the predictive graph for these three methodologies can match the actual value well, and linear regression performs the best. Considering the rapid development of machine learning methods, adopting alternative methods deserve in-depth investigations.

Keywords

ARIMA, SARIMA, linear regression

References

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Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
Proceedings of the 2nd International Conference on Financial Technology and Business Analysis
ISBN (Print)
978-1-83558-223-7
ISBN (Online)
978-1-83558-224-4
Published Date
28 December 2023
Series
Advances in Economics, Management and Political Sciences
ISSN (Print)
2754-1169
ISSN (Online)
2754-1177
DOI
10.54254/2754-1169/61/20230776
Copyright
© 2023 The Author(s)
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated