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AI 2021, 2 496 36. Zhao, Y.; Chen, Z. Forecasting stock price movement: New evidence from a novel hybrid deep learning model. J. Asian Bus. Econ. Studies 2021. ahead-of-print. [CrossRef] 37. Jain, A.; Tripathi, S.; Dwivedi, H.D.; Saxena, P. Forecasting Price of Cryptocurrencies Using Tweets Sentiment Analysis. In Proceedings of the 11th International Conference on Contemporary Computing (IC3), Noida, India, 2–4 August 2018; pp. 1–7. [CrossRef] 38. Wu, C.-H.; Lu, C.-C.; Ma, Y.-F.; Lu, R.-S. A New Forecasting Framework for Bitcoin Price with LSTM. In Proceedings of the IEEE International Conference on Data Mining Workshops (ICDMW), Singapore, 17–20 November 2018; pp. 168–175. [CrossRef] 39. Yamak, P.T.; Yujian, L.; Gadosey, P.K. A Comparison between ARIMA, LSTM, and GRU for Time Series Forecasting. In Proceedings of the 2nd International Conference on Algorithms, Computing and Artificial Intelligence, Sanya, China, 20–22 December 2019; pp. 49–55. [CrossRef] 40. McNally, S.; Roche, J.; Caton, S. Predicting the Price of Bitcoin Using Machine Learning. In Proceedings of the 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP), Cambridge, UK, 21–23 March 2018; pp. 339–343. [CrossRef] 41. Lazo, J.G.L.; Medina, G.H.H.; Guevara, A.V.; Talavera, A.; Otero, A.N.; Cordova, E.A. Support System to Investment Management in Cryptocurrencies. In Proceedings of the 2019 7th International Engineering, Sciences and Technology Conference, IESTEC, Panama, Panama, 9–11 October 2019; pp. 376–381. [CrossRef] 42. Available online: https://www.marketwatch.com (accessed on 16 June 2021). 43. Greff, K.; Srivastava, R.K.; Koutnik, J.; Steunebrink, B.R.; Schmidhuber, J. LSTM: A Search Space Odyssey. IEEE Trans. Neural Netw. Learn. Syst. 2017, 28, 2222–2232. [CrossRef] 44. Sherratt, F.; Plummer, A.; Iravani, P. Understanding LSTM Network Behaviour of IMU-Based Locomotion Mode Recognition for Applications in Prostheses and Wearables. Sensors 2021, 21, 1264. [CrossRef] [PubMed] 45. Jozefowicz, R.; Zaremba, W.; Sutskever, I. An empirical exploration of Recurrent Network architectures. In Proceedings of the 32nd International Conference on Machine Learning (ICML), Lille, France, 6–11 July 2015; Volume 3, pp. 2332–2340. 46. Le, X.-H.; Ho, H.V.; Lee, G.; Jung, S. Application of Long Short-Term Memory (LSTM) Neural Network for Flood Forecasting. Water 2019, 11, 1387. [CrossRef] 47. Li, X.; Peng, L.; Yao, X.; Cui, S.; Hu, Y.; You, C.; Chi, T. Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation. Environ. Pollut. 2017, 231, 997–1004. [CrossRef] 48. Kwak, G.H.; Park, C.W.; Ahn, H.Y.; Na, S.; Il, L.; Do, K.; Park, N.W. Potential of bidirectional long short-term memory networks for crop classification with multitemporal remote sensing images. Korean J. Remote. Sens. 2020, 36, 515–525. [CrossRef] 49. Althelaya, K.A.; El-Alfy, E.-S.M.; Mohammed, S. Evaluation of bidirectional LSTM for short-and long-term stock market prediction. In Proceedings of the 9th International Conference on Information and Communication Systems (ICICS), Irbid, Jordan., 3–5 April 2018. [CrossRef] 50. Yildirim, Ö. A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification. Comput. Biol. Med. 2018, 96, 189–202. [CrossRef] [PubMed] 51. Yang, S. Research on Network Behavior Anomaly Analysis Based on Bidirectional LSTM. In Proceedings of the IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), Chengdu, China, 15–17 March 2019. [CrossRef] 52. Dey, R.; Salemt, F.M. Gate-variants of Gated Recurrent Unit (GRU) neural networks. In Proceedings of the IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS), Boston, MA, USA, 6–9 August 2017. [CrossRef] 53. Zhao, R.; Wang, D.; Yan, R.; Mao, K.; Shen, F.; Wang, J. Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks. IEEE Trans. Ind. Electron. 2018, 65, 1539–1548. [CrossRef] 54. Su, Y.; Kuo, C.-C.J. On extended long short-term memory and dependent bidirectional recurrent neural network. Neurocomputing 2019, 356, 151–161. [CrossRef]PDF Image | Novel Cryptocurrency Price Prediction Model Using GRU
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