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All Journal Parameter: Journal of Statistics
Larasati, Kinanti Dhea
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Computer Science & IT Decision Sciences, Operations Research & Management Mathematics

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Forecasting Bitcoin Price Based on Blockchain Information Using Long-Short Term Method Larasati, Kinanti Dhea; Primandari, Arum Handini
Parameter: Journal of Statistics Vol. 1 No. 1 (2021)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (394.37 KB) | DOI: 10.22487/27765660.2021.v1.i1.15389

Abstract

Since its founding in 2008, Bitcoin (financial code: BTC) has emerged as a digital currency in market cap and continues to attract investors and policymakers' attention. In recent years, BTC has high price volatility, a substantial increase in 2016, followed by a significant decline in 2018. Unlike stock markets, BTC is open for 24x7 dan has no closing period. It means everyone can trade it for any time. However, this flexibility carries investment risk. This research attempts to forecast BTC's price by considering the blockchain's information to minimize the risk. We employ Long-Short Term Memory (LSTM), the artificial Recurrent Neural Network (RNN) architecture. Its model can avoid long-term problems. The data used is BTC's price and blockchain information data from August 4, 2018, to January 21, 2020. The model with 20 neurons and 500 epochs has the smallest MSE value. Then a prediction has an accuracy rate of 91.07%.
Co-Authors Primandari, Arum Handini