2019
DOI: 10.1016/j.chaos.2018.11.014
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Cryptocurrency forecasting with deep learning chaotic neural networks

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Cited by 271 publications
(131 citation statements)
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References 37 publications
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“…Deep learning techniques have been employed to forecast the price of Bitcoin, Digital Cash and Ripple in [16], where they have demonstrated that long-short term memory neural network (LSTM) performs better than generalized regression neural networks. The RMSE obtained using deep learning LSTM networks for Bitcoin, Digital Cash and Ripple are 2.75 × 10 3 19.2923 and 0.0499.…”
Section: Comparison With Previous Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…Deep learning techniques have been employed to forecast the price of Bitcoin, Digital Cash and Ripple in [16], where they have demonstrated that long-short term memory neural network (LSTM) performs better than generalized regression neural networks. The RMSE obtained using deep learning LSTM networks for Bitcoin, Digital Cash and Ripple are 2.75 × 10 3 19.2923 and 0.0499.…”
Section: Comparison With Previous Resultsmentioning
confidence: 99%
“…Lahmiri et al [16] Kim et al [17] have analyzed user comments in online cryptocurrency communities to predict fluctuation in the prices of the cryptocurrency and in the number of transactions thereof. The accuracy achieved for the predicted fluctuation in Bitcoin price and in Bitcoin transaction are 50.538% and 48.387% for 13 days.…”
Section: Related Workmentioning
confidence: 99%
“…Applying machine learning and DL models to predict the trends of cryptocurrencies' prices is an attractive research problem which is emerging in the literature (see Table 6). Lahmiri and Bekiros [82], for example, applied deep learning methods for the prediction of price of cryptocurrencies including Bitcoin, Digital Cash and Ripple. They compare the predictive performance of LSTM and GRNN.…”
Section: Cryptocurrencymentioning
confidence: 99%
“…Vector Autoregressive model and multivariate vector autoregressive model [2,16,17,18,19,20]. With the development of big data technology and artificial intelligence, a growing body of empirical studies has applied machine learning models to classification and prediction.…”
Section: Introductionmentioning
confidence: 99%