Improved LSTM Method of Predicting Cryptocurrency Price Using Short-Term Data

Risna Sari (Dept. of Information Technology, Universitas Amikom Yogyakarta)
Kusrini Kusrini (Dept. of Information Technology, Universitas Amikom Yogyakarta)
Tonny Hidayat (Dept. of Information Technology, Universitas Amikom Yogyakarta)
Theofanis Orphanoudakis (School of Science and Technology, Hellenic Open University, Patras)

IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
Vol 17, No 1 (2023): January

As cryptocurrencies develop, it cannot be denied that crypto prices are volatile. One of the influencing factors is the increasing volume of transactions which attracts the interest of researchers to conduct research in developing coin price predictions from cryptocurrencies. The method, algorithm and amount of data affect the prediction results. In this study, prediction modelling will be carried out using the LSTM method and short-term data. This study will conduct two experiments using the simple LSTM method and utilising multivariate time series with LSTM. The smallest predicted value is obtained using an 80/20 data allocation distribution scenario, input layer LSTM = 360, Epoch = 500, a Solana coin with RMSE = 0.111, R2 = 0.9962. It can be interpreted that short-term data can be used in making predictive models. Still, special attention needs to be paid to the characteristics of the dataset used and the modelling methodology, and it is hoped that the results of this study can be used in further research.