Intelligent method to cryptocurrency price variation forecasting

Abstract

Nowadays, accurate prediction of cryptocurrency price variation based on their important role in the world economy is an important and challenging issue. In this study, various parameters that affect the cryptocurrency value have been considered. For the first phase, four major price features of digital currencies have been analysed to determine the effect of each feature on the volatility prediction of future days. This study aims to understand and identify daily trends in the cryptocurrency market while gaining insight into optimal features surrounding their price. For the second phase, the price variation has been predicted with the highest possible accuracy with a new intelligent method. The proposed method consists of a neural network-based prediction algorithm and particle swarm optimisation. The obtained results show the capbility of the proposed method.

Keywords

• pricing
• particle swarm optimisation
• cryptography
• economic forecasting
• price features
• digital currencies
• volatility prediction
• cryptocurrency market
• optimal features
• intelligent method
• neural network-based prediction algorithm
• particle swarm optimisation
• cryptocurrency price variation forecasting
• world economy
• cryptocurrency value