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Ethereum Price Prediction with a GRU--Transformer Encoder Hybrid Model | ||
| Journal of Mathematics and Modeling in Finance | ||
| دوره 6، شماره 1، اردیبهشت 2026، صفحه 67-89 اصل مقاله (669.33 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22054/jmmf.2025.87542.1204 | ||
| نویسندگان | ||
| Yones Esmaeelzade Aghdam* 1؛ Hamid Mesgarani2؛ Ali Heidarvand2 | ||
| 1Department of Mathematics, Faculty of Statistics, Mathematics and Computer Science, Allameh Tabataba'i University, Tehran, Iran | ||
| 2Department of Mathematics, Shahid Rajaee Teacher Training University, Tehran, Iran | ||
| چکیده | ||
| Predicting the price of Ethereum remains a significant challenge due to the extreme volatility and nonlinear dynamics inherent in the cryptocurrency market. This study proposes a novel hybrid model that integrates a Gated Recurrent Unit (GRU) with a Transformer Encoder to effectively capture both short-term and long-term temporal dependencies for enhanced Ethereum price forecasting. The model was trained on daily historical data from 2017 to 2023. The dataset, sourced from Yahoo Finance, includes Ethereums open, high, and low prices, along with its trading volume. Additionally, Bitcoins closing price and two technical indicators, On-Balance Volume (OBV) and Average True Range (ATR), were incorporated. Pearson and Spearman correlation analyses confirmed strong interdependencies among the selected features. The model underwent training for 90 epochs, utilizing the Mean Squared Error (MSE) as the loss function and the Adam optimizer. Under identical experimental conditions, the proposed hybrid model significantly outperformed several baseline architectures, including standalone GRU, Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Transformer Encoder, and CNN–GRU hybrid models. Specifically, the model achieved a Mean Absolute Error (MAE) of 0.007199 (equivalent to $34.03), which is considerably lower than Ethereums average daily price fluctuation of $74.73. These findings demonstrate that the GRU–Transformer Encoder hybrid model is highly effective in extracting intricate patterns from volatile financial time series. Consequently, it can serve as a practical and robust tool for market trend analysis and risk management. | ||
| کلیدواژهها | ||
| Ethereum Price Prediction؛ Cryptocurrency Volatility؛ Gated Recurrent Unit؛ Transformer Encoder؛ Financial Time Series؛ Machine Learning | ||
| مراجع | ||
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[1] U. A. Auliyah, Cryptocurrencies price estimation using deep learning hybride model of LSTM–GRU, The Indonesian Journal of Computer Science, 13 (2024), no. 4. [2] K. Cho, B. van Merrienboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk ¨ and Y. Bengio, Learning phrase representations using RNN encoder–decoder for statistical machine translation, arXiv preprint arXiv:1406.1078, (2014). [3] J. Chung, C. Gulcehre, K. Cho and Y. Bengio, Empirical evaluation of gated recurrent neural networks on sequence modeling, arXiv preprint arXiv:1412.3555, (2014). [4] J. E. Granville, Granville’s New Key to Stock Market Profits, Pickle Partners Publishing, 2018. [5] R. Kaur, M. Uppal, D. Gupta, S. Juneja, S. Y. Arafat, J. Rashid, J. Kim and R. Alroobaea, Development of a cryptocurrency price prediction model: leveraging GRU and LSTM for Bitcoin, Litecoin and Ethereum, PeerJ Computer Science, 11 (2025), e2675. [6] D. P. Kingma and J. Ba, Adam: A method for stochastic optimization, arXiv preprint arXiv:1412.6980, (2014). [7] M. C. Lee, Temporal Fusion Transformer-based trading strategy for multi-crypto assets using on-chain and technical indicators, Systems, 13 (2025), no. 6, 474. [8] E. Mahdi, C. Martin-Barreiro and X. Cabezas, A novel hybrid approach using an attention-based Transformer+GRU model for predicting cryptocurrency prices, Mathematics, 13 (2025), no. 9, 1484. [9] S. Monish, M. Mohta and S. Rangaswamy, Ethereum price prediction using machine learning techniques – A comparative study, International Journal of Engineering Applied Sciences and Technology, 7 (2022), 137–142. [10] A. Mohammadjafari, Comparative study of Bitcoin price prediction, arXiv preprint arXiv:2405.08089, (2024). [11] K. Murray, A. Rossi, D. Carraro and A. Visentin, On forecasting cryptocurrency prices: A comparison of machine learning, deep learning, and ensembles, Forecasting, 5 (2023), no. 1, 196–209. [12] A. Saputra, A. N. Hidayat and D. Siahaan, Ethereum price prediction using LSTM and GRU, Proc. 5th Int. Conf. on Computer and Communication Engineering Technology (CCET), (2025). [13] S. Siami-Namini and A. Siami Namin, Forecasting economics and financial time series: ARIMA vs. LSTM, arXiv preprint arXiv:1803.06386, (2018). [14] S. Tanwar, N. P. Patel, S. N. Patel, J. R. Patel, G. Sharma and I. E. Davidson, Deep learning-based cryptocurrency price prediction scheme with inter-dependent relations, IEEE Access, 9 (2021), 138633–138646. [15] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser and I. Polosukhin, Attention is all you need, Advances in Neural Information Processing Systems, 30 (2017). [16] J. W. Wilder Jr., New Concepts in Technical Trading Systems, Trend Research, Greensboro, NC, 1978. | ||
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