Cryptocurrency forecasting with deep learning chaotic neural networks

Salim Lahmiri and Stelios Bekiros

Chaos, Solitons & Fractals, 2019, vol. 118, issue C, 35-40

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.

Keywords: Digital currencies; Deep learning; Fractality; Neural networks; Chaos; Forecasting (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (86)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:118:y:2019:i:c:p:35-40

DOI: 10.1016/j.chaos.2018.11.014

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