Cryptocurrency forecasting with deep learning chaotic neural networks

By Salim Lahmiri, Stelios D. Bekiros. Chaos, Solitons & Fractals, 2019

312 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics, Management Science and Operations Research, Signal Processing

View Paper: https://www.semanticscholar.org/paper/b0b480ec083e950eb8923d7e451b0ea93c4e71f3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Aytaç Altan
2. Seçkin Karasu
3. M. Naeem
4. Anne de Bruin
5. Md Iftekhar Hasan Chowdhury
6. Stelios D. Bekiros
7. Faruk Balli
8. Ş. Nazlıoğlu

In Management Science and Operations Research

1. Aytaç Altan
2. Seçkin Karasu
3. Anne de Bruin
4. Stelios D. Bekiros

In Signal Processing

1. Aytaç Altan
2. Seçkin Karasu
3. Stelios D. Bekiros

Citations in the corpus

• Digital currency forecasting with chaotic meta-heuristic bio-inspired signal processing techniques (2019, methodology): “The LLE algorithm which is given in [21] directly tests the exponential divergence of nearby trajectories.”
• Connectedness of cryptocurrencies and prevailing uncertainties (2020, background): “For instance, Lahmiri and Bekiros (2019) stress the chaotic properties of the most widely traded cryptocurrencies by revealing their fractal dynamics, long memory, and self-similarity.”