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• W2900967578 abstract "We implement deep learning techniques to forecast the price of the three most widely traded digital currencies i.e., Bitcoin, Digital Cash and Ripple. To the best of our knowledge, this is the first work to make use of deep learning in cryptocurrency prediction. The results from testing the existence of nonlinearity revealed that the time series of all digital currencies exhibit fractal dynamics, long memory and self-similarity. The predictability of long-short term memory neural network topologies (LSTM) is significantly higher when compared to the generalized regression neural architecture, set forth as our benchmark system. The latter failed to approximate global nonlinear hidden patterns regardless of the degree of contamination with noise, as they are based on Gaussian kernels suitable only for local approximation of non-stationary signals. Although the computational burden of the LSTM model is higher as opposed to brute force in nonlinear pattern recognition, eventually deep learning was found to be highly efficient in forecasting the inherent chaotic dynamics of cryptocurrency markets." @default.
• W2900967578 created "2018-11-29" @default.
• W2900967578 creator A5038937667 @default.
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• W2900967578 date "2019-01-01" @default.
• W2900967578 modified "2023-10-12" @default.
• W2900967578 title "Cryptocurrency forecasting with deep learning chaotic neural networks" @default.
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• W2900967578 doi "https://doi.org/10.1016/j.chaos.2018.11.014" @default.
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