Bitcoin Price Prediction Using Autoregressive Integrated Moving Average (ARIMA) Model

Chunyu Wen, Tianer Li, Zhiyang Qiu
{"title":"Bitcoin Price Prediction Using Autoregressive Integrated Moving Average (ARIMA) Model","authors":"Chunyu Wen, Tianer Li, Zhiyang Qiu","doi":"10.1145/3573834.3574559","DOIUrl":null,"url":null,"abstract":"As the world's most valuable cryptocurrency, Bitcoin offers a new opportunity for price forecasting because of its high volatility, which is much higher compared to traditional currencies. Since bitcoin prices fluctuate randomly over time, we can use a time series model to predict the price of bitcoin. For this purpose, we use the ARIMA model to predict the future bitcoin price based on past prices. The basic idea of the ARIMA model is that the data series of the predicted object over time is considered as a random series, and some mathematical model is used to approximate this series. Once this model is determined, it is possible to predict the future values from the past values of the time series as well as the present values. The model achieves high accuracy and robustness. The result shows that there's inevitable deviation every time the price trend is having acute change, and the deviation of actual value to predicted one is positively correlated to the average value.","PeriodicalId":345434,"journal":{"name":"Proceedings of the 4th International Conference on Advanced Information Science and System","volume":"29 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 4th International Conference on Advanced Information Science and System","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3573834.3574559","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

As the world's most valuable cryptocurrency, Bitcoin offers a new opportunity for price forecasting because of its high volatility, which is much higher compared to traditional currencies. Since bitcoin prices fluctuate randomly over time, we can use a time series model to predict the price of bitcoin. For this purpose, we use the ARIMA model to predict the future bitcoin price based on past prices. The basic idea of the ARIMA model is that the data series of the predicted object over time is considered as a random series, and some mathematical model is used to approximate this series. Once this model is determined, it is possible to predict the future values from the past values of the time series as well as the present values. The model achieves high accuracy and robustness. The result shows that there's inevitable deviation every time the price trend is having acute change, and the deviation of actual value to predicted one is positively correlated to the average value.
基于自回归综合移动平均(ARIMA)模型的比特币价格预测
作为世界上最有价值的加密货币,比特币的高波动性比传统货币高得多,为价格预测提供了新的机会。由于比特币价格随时间随机波动,我们可以使用时间序列模型来预测比特币的价格。为此,我们使用ARIMA模型根据过去的价格预测未来的比特币价格。ARIMA模型的基本思想是将被预测对象的数据序列随时间的变化看作一个随机序列,并使用一些数学模型来近似这个序列。一旦确定了这个模型,就可以从时间序列的过去值和现在值来预测未来值。该模型具有较高的精度和鲁棒性。结果表明,每次价格走势发生剧烈变化时都不可避免地存在偏差,实际值与预测值的偏差与平均值呈正相关。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信