QORA-ANN: Quasi Opposition Based Rao Algorithm and Artificial Neural Network for Cryptocurrency Prediction

Abstract

The cryptocurrency price movement behaves randomly and fluctuates like other stock markets. Prediction of cryptocurrency is a recent area of research interest and budding fast. The underlying nonlinearities in its price series make its prediction challenging. Sophisticated methodologies for accurate prediction of cryptocurrency are highly desired. Artificial neural networks (ANNs) are good approximators, however their accuracy is greatly subjective to optimal network structure and learning method. This article designs optimal ANNs for efficient cryptocurrency prediction using quasi opposition based Rao algorithms, i.e. QORA-ANN. The model explores a set of potential ANNs in the search space and lands at an optimal network through the evolving process. Historical data from four emerging cryptocurrencies such as Bitcoin, Litecoin, Ethereum, and Ripple are used to evaluate the QORA-ANN. The prediction ability of the proposed approach is compared with few similar methods such as ANN trained with genetic algorithm, differential evolution and particle swarm optimization (i.e. ANN-GA, ANN-DE, ANN-PSO), support vector machine (SVM), and multilayer perceptron (MLP). From exhaustive simulation studies and comparative result analysis it is found that the QORA-ANN method performed better than others and hence can be suggested as an efficient tool for cryptocurrencies prediction.