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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
Theofanis Orphanoudakis
School of Science and Technology, Hellenic Open University, Patras
Computer Science & IT Control & Systems Engineering

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Improved LSTM Method of Predicting Cryptocurrency Price Using Short-Term Data Risna Sari; Kusrini Kusrini; Tonny Hidayat; Theofanis Orphanoudakis
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 17, No 1 (2023): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.80776

Abstract

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.
Co-Authors Kusrini Kusrini Risna Sari TONNY HIDAYAT