DLCP2F: a DL-based cryptocurrency price prediction framework
Abstract Cryptocurrencies are distributed digital currencies that have emerged as a consequence of financial technology advancement. In 2017, cryptocurrencies have shown a huge rise in their market capitalization and popularity. They are now employed in today’s financial systems as individual invest...
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| Format: | Article |
| Language: | English |
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Springer
2022-10-01
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| Series: | Discover Artificial Intelligence |
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| Online Access: | https://doi.org/10.1007/s44163-022-00036-2 |
| _version_ | 1797986075981905920 |
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| author | Abdussalam Aljadani |
| author_facet | Abdussalam Aljadani |
| author_sort | Abdussalam Aljadani |
| collection | DOAJ |
| description | Abstract Cryptocurrencies are distributed digital currencies that have emerged as a consequence of financial technology advancement. In 2017, cryptocurrencies have shown a huge rise in their market capitalization and popularity. They are now employed in today’s financial systems as individual investors, corporate firms, and big institutions are heavily investing in them. However, this industry is less stable than traditional currency markets. It can be affected by several legal, sentimental, and technical factors, so it is highly volatile, dynamic, uncertain, and unpredictable, hence, accurate forecasting is essential. Recently, cryptocurrency price prediction becomes a trending research topic globally. Various machine and deep learning algorithms, e.g., Neural Networks (NN), Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM) were utilized to analyze the factors influencing the prices of the cryptocurrencies and accordingly predict them. This paper suggests a five-phase framework for cryptocurrency price prediction based on two state-of-the-art deep learning architectures (i.e., BiLSTM and GRU). The current study uses three public real-time cryptocurrency datasets from “Yahoo Finance”. Bidirectional Long Short-Term Memory and Gated Recurrent Unit deep learning-based algorithms are used to forecast the prices of three popular cryptocurrencies (i.e., Bitcoin, Ethereum, and Cardano). The Grid Search approach is used for the hyperparameters optimization processes. Results indicate that GRU outperformed the BiLSTM algorithm for Bitcoin, Ethereum, and Cardano, respectively. The lowest RMSE for the GRU model was found to be 0.01711, 0.02662, and 0.00852 for Bitcoin, Ethereum, and Cardano, respectively. Experimental results proved the significant performance of the proposed framework that achieves the minimum MSE and RMSE values. |
| first_indexed | 2024-04-11T07:28:21Z |
| format | Article |
| id | doaj.art-142b4a12357640f1a9f749196e22c891 |
| institution | Directory Open Access Journal |
| issn | 2731-0809 |
| language | English |
| last_indexed | 2024-04-11T07:28:21Z |
| publishDate | 2022-10-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Artificial Intelligence |
| spelling | doaj.art-142b4a12357640f1a9f749196e22c8912022-12-22T04:37:01ZengSpringerDiscover Artificial Intelligence2731-08092022-10-012111910.1007/s44163-022-00036-2DLCP2F: a DL-based cryptocurrency price prediction frameworkAbdussalam Aljadani0Department of Management, College of Business Administration in Yanbu, Taibah UniversityAbstract Cryptocurrencies are distributed digital currencies that have emerged as a consequence of financial technology advancement. In 2017, cryptocurrencies have shown a huge rise in their market capitalization and popularity. They are now employed in today’s financial systems as individual investors, corporate firms, and big institutions are heavily investing in them. However, this industry is less stable than traditional currency markets. It can be affected by several legal, sentimental, and technical factors, so it is highly volatile, dynamic, uncertain, and unpredictable, hence, accurate forecasting is essential. Recently, cryptocurrency price prediction becomes a trending research topic globally. Various machine and deep learning algorithms, e.g., Neural Networks (NN), Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM) were utilized to analyze the factors influencing the prices of the cryptocurrencies and accordingly predict them. This paper suggests a five-phase framework for cryptocurrency price prediction based on two state-of-the-art deep learning architectures (i.e., BiLSTM and GRU). The current study uses three public real-time cryptocurrency datasets from “Yahoo Finance”. Bidirectional Long Short-Term Memory and Gated Recurrent Unit deep learning-based algorithms are used to forecast the prices of three popular cryptocurrencies (i.e., Bitcoin, Ethereum, and Cardano). The Grid Search approach is used for the hyperparameters optimization processes. Results indicate that GRU outperformed the BiLSTM algorithm for Bitcoin, Ethereum, and Cardano, respectively. The lowest RMSE for the GRU model was found to be 0.01711, 0.02662, and 0.00852 for Bitcoin, Ethereum, and Cardano, respectively. Experimental results proved the significant performance of the proposed framework that achieves the minimum MSE and RMSE values.https://doi.org/10.1007/s44163-022-00036-2Bidirectional Long Short-Term MemoryCryptocurrencyDeep learningGated Recurrent UnitRecurrent Neural NetworkTime series |
| spellingShingle | Abdussalam Aljadani DLCP2F: a DL-based cryptocurrency price prediction framework Discover Artificial Intelligence Bidirectional Long Short-Term Memory Cryptocurrency Deep learning Gated Recurrent Unit Recurrent Neural Network Time series |
| title | DLCP2F: a DL-based cryptocurrency price prediction framework |
| title_full | DLCP2F: a DL-based cryptocurrency price prediction framework |
| title_fullStr | DLCP2F: a DL-based cryptocurrency price prediction framework |
| title_full_unstemmed | DLCP2F: a DL-based cryptocurrency price prediction framework |
| title_short | DLCP2F: a DL-based cryptocurrency price prediction framework |
| title_sort | dlcp2f a dl based cryptocurrency price prediction framework |
| topic | Bidirectional Long Short-Term Memory Cryptocurrency Deep learning Gated Recurrent Unit Recurrent Neural Network Time series |
| url | https://doi.org/10.1007/s44163-022-00036-2 |
| work_keys_str_mv | AT abdussalamaljadani dlcp2fadlbasedcryptocurrencypricepredictionframework |