Kinderis, Marius, Bezbradica, MarijaORCID: 0000-0001-9366-5113 and Crane, MartinORCID: 0000-0001-7598-3126
(2018)
Bitcoin currency fluctuation.
In: COMPLEXIS 2018 - 3rd International Conference on Complexity, Future Information Systems and Risk, 20-21 Mar 2018, Madeira, Portugal.
ISBN 978-989-758-297-4
Predicting currency prices remains a difficult endeavour. Investors are continually seeking new ways to extract
meaningful information about the future direction of price changes. Recently, cryptocurrencies have attracted
huge attention due to their unique way of transferring value as well as its value as a hedge. A method proposed
in this project involves using data mining techniques: mining text documents such as news articles and tweets
try to infer the relationship between information contained in such items and cryptocurrency price direction.
The Long Short-Term Memory Recurrent Neural Network (LSTM RNN) assists in creating a hybrid model
which comprises of sentiment analysis techniques, as well as a predictive machine learning model. The success
of the model was evaluated within the context of predicting the direction of Bitcoin price changes. Findings
reported here reveal that our system yields more accurate and real-time predictions of Bitcoin price fluctuations
when compared to other existing models in the market.
Ramachandran, Muthu, Chang, Victor and Méndez Muñoz, Victor, (eds.)
Proceedings of the 3rd International Conference on Complexity, Future Information Systems and Risk. Proceedings of the International Conference on Complexity, Future Information Systems and Risk
1.
Scitepress. ISBN 978-989-758-297-4