Novel deep learning model for bitcoin price prediction by multiplicative LSTM with attention mechanism and technical indicators

【Author】 Ahamed, S. Aarif; Ravi, Chandrasekar

【Source】INTERNATIONAL JOURNAL OF ENGINEERING SYSTEMS MODELLING AND SIMULATION

【影响因子】0.000

【Abstract】Starting from the earlier civilisation till date, money plays a crucial part in the transfer of goods and services. With this digital world, the money also changes its faces from paper money to digital currency called cryptocurrency without any central bank, which runs on top of the technology called blockchain. The trendiest cryptocurrency is bitcoin. Forecasting the daily price is a challenging task due to its nonlinearity. Most of the researchers tried to predict using various statistical and machine learning models which were not satisfactory because of its large dataset with more noise. The intention is to design a deep learning multiplicative long short-term memory model to estimate the price of bitcoin with an attention mechanism using technical indicators which gives better accuracy and a very less error rate. The proposed model is compared with some existing models, say long short-term memory, peephole, gated recurrent unit and multiplicative long short-term memory on the presence and absence of technical indicators. The comparative result shows that the proposed model outperforms the existing models in terms of mean square error, root mean square error and mean absolute error when evaluated with two benchmark datasets.

【Keywords】cryptocurrency; bitcoin price prediction; bitcoin; deep learning; technical indicator; attention mechanism; multiplicative LSTM

【发表时间】2022

【收录时间】2022-06-26

【文献类型】实证性文章

【主题类别】

区块链治理-市场治理-数字货币