Patent Information

Abstract

Abstract: After the boom and bust of blockchain in recent years, cryptocurrency has been increasingly regarded as an investment asset. Because of its highly volatile nature, there is a need for good predictions on which to base investment decisions. Although existing studies have leveraged machine learning for more accurate cryptocurrency price prediction, few have focused on the feasibility of applying different modelling techniques to samples with different data structures and dimensional features. To predict cryptocurrency price at different frequencies using machine learning techniques, we first classify cryptocurrency price by daily price and high-frequency price. A set of high-dimension features including property and network, trading and market, attention and gold spot price are used for cryptocurrency daily price prediction, while the basic trading features acquired from a cryptocurrency exchange are used for 5-minute interval price prediction. Price control by a number of organizations has had a significant impact on the level of one main or central control over them, affecting relationships with other businesses and international trade. Furthermore, the ever-changing oscillations suggest a more accurate means of projecting this price is desperately needed. Thus, using deep learning techniques such as the recurrent neural network (RNN) and the long short-term memory (LSTM), gated recurrent unit (GRU), which are effective learning models for training data, we must design a method for the accurate prediction of by considering various factors such as market cap, maximum supply and, volume, circulating supply. The proposed method is written in Python and tested on benchmark datasets. The results show that the proposed method can be used to make reliable predictions. Thus, the neural network, which has been used by academics in numerous fields over the past ten years as one of the intelligent data mining tools

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