Abstract
Bitcoin is one of the new currencies in online transactions that was found to be one of the cryptocurrencies that has fluctuating volatility, so researchers are looking for the determinants of bitcoin price. In this paper, we adopt a Long Short-Term Memory Networks (LSTM) approach to evaluate the effect of investor attention on bitcoin returns by constructing an aggregate investor attention proxy. In addition, LSTM is a method of Recurrent Neural Network (RNN) to deal with the problem of long-term dependence on RNN. The main objective of this research is to design, implement, and visualize Bitcoin Price Prediction using the LSTM method. The data this study are daily bitcoin cover data and tweets taken from twitter. The measure of the accuracy of the model used is the Root Mean Square Error (RMSE). The results show that the RMSE value is quite small, meaning that it is quite good in modeling bitcoin predictions.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
