Time-Series Prediction of Cryptocurrency Market using Machine Learning Techniques

Iqbal, Mahir and Iqbal, Muhammad Shuaib and Jaskani, Fawwad Hassan and Iqbal, Khurum and Hassan, Ali (2021) Time-Series Prediction of Cryptocurrency Market using Machine Learning Techniques. EAI Endorsed Transactions on Creative Technologies. e4. ISSN 2409-9708

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Abstract

In the market of cryptocurrency the Bitcoins are the first currency which has gain the significant importance. To predict the market price and stability of Bitcoin in Crypto-market, a machine learning based time series analysis has been applied. Time-series analysis can predict the future ups and downs in the price of Bitcoin. For this purpose we have used ARIMA, FBProphet, XG Boosting for time series analysis as a machine learning techniques. The parameters on the basis of which we have evaluated these models are Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and R2. We conduct experiments on these three techniques but after conducting time series analysis, ARIMA considered as the best model for forecasting Bitcoin price in the crypto-market with RMSE score of 322.4 and MAE score of 227.3. Additionally, this research can be helpful for investors of crypto-market.

Item Type: Article
Uncontrolled Keywords: data mining, visualization, machine learning, Emerging Nature Inspired Computing
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
T Technology > T Technology (General)
Depositing User: EAI Editor IV
Date Deposited: 15 Jul 2021 11:37
Last Modified: 15 Jul 2021 11:37
URI: https://eprints.eudl.eu/id/eprint/4740

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