Cryptocurrency Price Prediction with Convolutional Neural Network and Stacked Gated Recurrent Unit
Virtual currencies have been declared as one of the financial assets that are widely recognized as exchange currencies. The cryptocurrency trades caught the attention of investors as cryptocurrencies can be considered as highly profitable investments. To optimize the profit of the cryptocurrency inv...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Data |
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| Online Access: | https://www.mdpi.com/2306-5729/7/11/149 |
| _version_ | 1827646888784953344 |
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| author | Chuen Yik Kang Chin Poo Lee Kian Ming Lim |
| author_facet | Chuen Yik Kang Chin Poo Lee Kian Ming Lim |
| author_sort | Chuen Yik Kang |
| collection | DOAJ |
| description | Virtual currencies have been declared as one of the financial assets that are widely recognized as exchange currencies. The cryptocurrency trades caught the attention of investors as cryptocurrencies can be considered as highly profitable investments. To optimize the profit of the cryptocurrency investments, accurate price prediction is essential. In view of the fact that the price prediction is a time series task, a hybrid deep learning model is proposed to predict the future price of the cryptocurrency. The hybrid model integrates a 1-dimensional convolutional neural network and stacked gated recurrent unit (1DCNN-GRU). Given the cryptocurrency price data over the time, the 1-dimensional convolutional neural network encodes the data into a high-level discriminative representation. Subsequently, the stacked gated recurrent unit captures the long-range dependencies of the representation. The proposed hybrid model was evaluated on three different cryptocurrency datasets, namely Bitcoin, Ethereum, and Ripple. Experimental results demonstrated that the proposed 1DCNN-GRU model outperformed the existing methods with the lowest RMSE values of 43.933 on the Bitcoin dataset, 3.511 on the Ethereum dataset, and 0.00128 on the Ripple dataset. |
| first_indexed | 2024-03-09T19:09:42Z |
| format | Article |
| id | doaj.art-c19b84a4757a43cbb2dcb66f8b18fa1a |
| institution | Directory Open Access Journal |
| issn | 2306-5729 |
| language | English |
| last_indexed | 2024-03-09T19:09:42Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Data |
| spelling | doaj.art-c19b84a4757a43cbb2dcb66f8b18fa1a2023-11-24T04:16:53ZengMDPI AGData2306-57292022-10-0171114910.3390/data7110149Cryptocurrency Price Prediction with Convolutional Neural Network and Stacked Gated Recurrent UnitChuen Yik Kang0Chin Poo Lee1Kian Ming Lim2Faculty of Information Science and Technology, Multimedia University, Melaka 75450, MalaysiaFaculty of Information Science and Technology, Multimedia University, Melaka 75450, MalaysiaFaculty of Information Science and Technology, Multimedia University, Melaka 75450, MalaysiaVirtual currencies have been declared as one of the financial assets that are widely recognized as exchange currencies. The cryptocurrency trades caught the attention of investors as cryptocurrencies can be considered as highly profitable investments. To optimize the profit of the cryptocurrency investments, accurate price prediction is essential. In view of the fact that the price prediction is a time series task, a hybrid deep learning model is proposed to predict the future price of the cryptocurrency. The hybrid model integrates a 1-dimensional convolutional neural network and stacked gated recurrent unit (1DCNN-GRU). Given the cryptocurrency price data over the time, the 1-dimensional convolutional neural network encodes the data into a high-level discriminative representation. Subsequently, the stacked gated recurrent unit captures the long-range dependencies of the representation. The proposed hybrid model was evaluated on three different cryptocurrency datasets, namely Bitcoin, Ethereum, and Ripple. Experimental results demonstrated that the proposed 1DCNN-GRU model outperformed the existing methods with the lowest RMSE values of 43.933 on the Bitcoin dataset, 3.511 on the Ethereum dataset, and 0.00128 on the Ripple dataset.https://www.mdpi.com/2306-5729/7/11/149cryptocurrency price predictionprice predictionconvolutional neural networkgated recurrent unitCNNGRU |
| spellingShingle | Chuen Yik Kang Chin Poo Lee Kian Ming Lim Cryptocurrency Price Prediction with Convolutional Neural Network and Stacked Gated Recurrent Unit Data cryptocurrency price prediction price prediction convolutional neural network gated recurrent unit CNN GRU |
| title | Cryptocurrency Price Prediction with Convolutional Neural Network and Stacked Gated Recurrent Unit |
| title_full | Cryptocurrency Price Prediction with Convolutional Neural Network and Stacked Gated Recurrent Unit |
| title_fullStr | Cryptocurrency Price Prediction with Convolutional Neural Network and Stacked Gated Recurrent Unit |
| title_full_unstemmed | Cryptocurrency Price Prediction with Convolutional Neural Network and Stacked Gated Recurrent Unit |
| title_short | Cryptocurrency Price Prediction with Convolutional Neural Network and Stacked Gated Recurrent Unit |
| title_sort | cryptocurrency price prediction with convolutional neural network and stacked gated recurrent unit |
| topic | cryptocurrency price prediction price prediction convolutional neural network gated recurrent unit CNN GRU |
| url | https://www.mdpi.com/2306-5729/7/11/149 |
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