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Bitcoin closing price movement prediction with optimal functional link neural networks

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Abstract

Bitcoin prediction is a recent area of research interest and growing fast. Since its inception, in a short period of time Bitcoin got wide popularity and considered as an investment asset. At present it is the most successful cryptocurrency compared to other altcoins dispersed in the world economy. The Bitcoin prices fluctuate like other stock markets due to inherent volatility. The investors’ confidence on Bitcoin rising fast and have been reflected on its prices. Though few computational intelligence methods are available, sophisticated methodologies for accurate prediction of Bitcoin are still lacking and need to be explored. Functional link neural network (FLN) is a flat network, offers lower computational complexity, and achieves enhanced input–output nonlinearity mapping through functional expansion of input signals. Basis functions such as Legendre, Trigonometric, Laguerre, and Chebyshev are commonly used polynomials in FLN for expansion of input signal dimension. In this article along with the weight and bias vector of FLNs, optimal number of polynomial functions for each category of basis function is selected by genetic algorithm during training process rather fixing them earlier. Therefore, an optimal FLN structure is crafted on fly from exploitation of training data. The optimal FLN models are used to predict the daily, weekly, and monthly closing prices of Bitcoin. A comparative study among optimal FLNs is carried out using evaluation metrics such as MAPE, NMSE, ARV, and U of Theil’s statistics. Finally, outcomes from experimental and comparative studies suggested the superiority of optimal FLN models for Bitcoin closing price prediction.

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Abbreviations

ANN:

Artificial neural network

ARV:

Average relative variance

ARIMA:

Auto-regressive moving average

BSE:

Bombay stock exchange

CFLN:

Chebysheb functional link neural network

CNN:

Convolutional neural network

DNN:

Deep neural network

DJIA:

Dow Jones industrial average

FLN:

Functional neural network

GA:

Genetic algorithm

LFLN:

Lagurre functional link neural network

LSTM:

Long short term memory

LMS:

Least square estimation

MAPE:

Mean absolute percentage of error

MLP:

Multilayer perceptron

NMSE:

Normalized mean squared error

PSO:

Particle swarm optimization

RNN:

Recurrent neural network

SVM:

Support vector machine

TFLN:

Trigonmetric functional neural network

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Authors and Affiliations

  1. Department of Computer Science and Engineering, CMR College of Engineering and Technology, Hyderabad, 501401, India

    Sarat Chandra Nayak

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  1. Sarat Chandra Nayak
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Correspondence to Sarat Chandra Nayak.

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Nayak, S.C. Bitcoin closing price movement prediction with optimal functional link neural networks. Evol. Intel. 15, 1825–1839 (2022). https://doi.org/10.1007/s12065-021-00592-z

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