Bitcoin Price Prediction using the Hybrid Convolutional Recurrent Model Architecture
Received: 24 July 2023 | Revised: 13 August 2023 | Accepted: 18 August 2023 | Online: 2 September 2023
Corresponding author: Omar M. Ahmed
The field of finance makes extensive use of real-time prediction of stock price tools, which are instruments that are put to use in the process of creating predictions. In this article, we attempt to predict the price of Bitcoin in a manner that is both accurate and reliable. Deep learning models, as opposed to more traditional methods, are used to manage enormous volumes of data and to generate predictions. The purpose of this research is to develop a method for predicting stock prices using the Hybrid Convolutional Recurrent Model (HCRM) architecture. This model architecture integrates the advantages of two separate deep learning models: The 1-Dimensional-Convolusional Neural Network (1D-CNN) and the Long-Short Term Memory (LSTM). The 1D-CNN is responsible for the feature extraction, while the LSTM is in charge of the temporal regression. The developed 1D-CNN-LSTM model has an outstanding performance in predicting stock values.
Keywords:1D-CNN, LSTM, Bitcoin, deep learning, prediction
O. B. Sezer, M. U. Gudelek, and A. M. Ozbayoglu, "Financial time series forecasting with deep learning : A systematic literature review: 2005–2019," Applied Soft Computing, vol. 90, May 2020, Art. no. 106181.
W. Long, Z. Lu, and L. Cui, "Deep learning-based feature engineering for stock price movement prediction," Knowledge-Based Systems, vol. 164, pp. 163–173, Jan. 2019.
Y. Kara, M. Acar Boyacioglu, and Ö. K. Baykan, "Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange," Expert Systems with Applications, vol. 38, no. 5, pp. 5311–5319, May 2011.
U. P. Gurav and S. Kotrappa, "Sentiment Aware Stock Price Forecasting using an SA-RNN-LBL Learning Model," Engineering, Technology & Applied Science Research, vol. 10, no. 5, pp. 6356–6361, Oct. 2020.
S. McNally, J. Roche, and S. Caton, "Predicting the Price of Bitcoin Using Machine Learning," in 2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP), Cambridge, UK, Mar. 2018, pp. 339–343.
M. Saad, J. Choi, D. Nyang, J. Kim, and A. Mohaisen, "Toward Characterizing Blockchain-Based Cryptocurrencies for Highly Accurate Predictions," IEEE Systems Journal, vol. 14, no. 1, pp. 321–332, Mar. 2020.
H. Jang and J. Lee, "An Empirical Study on Modeling and Prediction of Bitcoin Prices With Bayesian Neural Networks Based on Blockchain Information," IEEE Access, vol. 6, pp. 5427–5437, 2018.
J. Huisu, J. Lee, H. Ko, and W. Lee, "Predicting bitcoin prices by using rolling window LSTM model," in Proceedings of the KDD data science in Fintech Workshop, London, UK, Aug. 2018, pp. 19–23.
T. Shintate and L. Pichl, "Trend Prediction Classification for High Frequency Bitcoin Time Series with Deep Learning," Journal of Risk and Financial Management, vol. 12, no. 1, Mar. 2019, Art. no. 17.
Y. B. Kim et al., "Predicting Fluctuations in Cryptocurrency Transactions Based on User Comments and Replies," PLOS ONE, vol. 11, no. 8, 2016, Art. no. e0161197.
Srivinay, B. C. Manujakshi, M. G. Kabadi, and N. Naik, "A Hybrid Stock Price Prediction Model Based on PRE and Deep Neural Network," Data, vol. 7, no. 5, May 2022, Art. no. 51.
Md. E. Karim, Md. Foysal, and S. Das, "Stock Price Prediction Using Bi-LSTM and GRU-Based Hybrid Deep Learning Approach," in Proceedings of Third Doctoral Symposium on Computational Intelligence, Singapore, 2023, pp. 701–711.
O. Ahmed and A. Brifcani, "Gene Expression Classification Based on Deep Learning," in 2019 4th Scientific International Conference Najaf (SICN), Al-Najef, Iraq, Apr. 2019, pp. 145–149.
D. K. Suker, "Deep Learning CNN for the Prediction of Grain Orientations on EBSD Patterns of AA5083 Alloy," Engineering, Technology & Applied Science Research, vol. 12, no. 2, pp. 8393–8401, Apr. 2022.
N. T. T. Vu, N. P. Tran, and N. H. Nguyen, "Recurrent Neural Network-based Path Planning for an Excavator Arm under Varying Environment," Engineering, Technology & Applied Science Research, vol. 11, no. 3, pp. 7088–7093, Jun. 2021.
F. Qian and X. Chen, "Stock Prediction Based on LSTM under Different Stability," in 2019 IEEE 4th International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), Chengdu, China, Apr. 2019, pp. 483–486.
M. Ćalasan, S. H. E. Abdel Aleem, and A. F. Zobaa, "On the root mean square error (RMSE) calculation for parameter estimation of photovoltaic models: A novel exact analytical solution based on Lambert W function," Energy Conversion and Management, vol. 210, Apr. 2020, Art. no. 112716.
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Copyright (c) 2023 Omar M. Ahmed, Lailan M. Haji, Ayah M. Ahmed, Nashwan M. Salih
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