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Submitted
Dec 19, 2020
Accepted
Jan 30, 2021
Published
Jan 30, 2021
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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%.

Keywords

Bitcoin Blockchain Information Forecasting Long-Short Term Memory

Article Details

How to Cite
Larasati, K. D., & Primandari, A. H. (2021). Forecasting Bitcoin Price Based on Blockchain Information Using Long-Short Term Method. Parameter: Journal of Statistics, 1(1), 1-6. https://doi.org/10.22487/27765660.2021.v1.i1.15389

References

  1. Aldi, M. W., Jondri, & Aditsania, A. (2018). Analisis dan Implementasi Long Short Term Memory Neural Network untuk Prediksi Harga Bitcoin. e-Proceeding of Engineering, 3551-3552. https://repository.telkomuniversity.ac.id/pustaka/files/144393/jurnal_eproc/analisis-dan-implementasi-long-short-term-memory-neural-network-untuk-prediksi-harga-bitcoin.pdf.
  2. Al-Yahyaee, K. H.-J. (2019). Can Uncertainty Indices Predict Bitcoin Prices? A Revisited Analysis Using Partial and Multivariate Wavelet Approaches. North American Journal of Economics and Finance, 48. https://www.sciencedirect.com/science/article/pii/S1062940818306703.
  3. Bakar, N. A., & Rosbi, S. (2017). Autoregressive Integrated Moving Average (ARIMA) Model for Forecasting Cryptocurrency Exchange Rate in High Volatility Environment: A New Insight of Bitcoin Transaction. International Journal of Advanced Engineering Research and Science, 130.
  4. Budiman, H. (2016). Analisis Dan Perbandingan Akurasi Model Prediksi Rentet Waktu Support Vector Machines Dengan Support Vector Machines Particle Swarm Optimization Untuk Arus Lalu Lintas Jangka Pendek. SYSTEMIC, 21-22. http://jurnalsaintek.uinsby.ac.id/index.php/SYSTEMIC/article/view/103.
  5. Farras, B. (2018). Harga Bitcoin Anjlok, Pengangguran Bertambah. Jakarta: CNBC Indonesia.
  6. Fischer, T., & Krauss, C. (2018). Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research, 270(2), 654-669.
  7. Jang, H., & Lee, J. (2018). An Empirical Study on Modeling and Prediction of Bitcoin Prices With Bayesian Neural Networks Based on Blockchain Information. IEEE, vol. 6, ISSN: 2169-3536, 5427-5437. https://ieeexplore.ieee.org/document/8125674.
  8. Kratzert, F., Klotz, D., Brenner, C., Schulz, K., & Herrnegger, M. (2018). Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks. Hydrology and Earth System Sciences, 6005-6022.
  9. Ma, X. (2015). Long Short-Term Memory Neural Network for Traffic Speed Prediction Using Remote Microwave Sensor Data. Transportation Research Part C, 191. https://www.sciencedirect.com/science/article/pii/S0968090X15000935.
  10. McNally, S., Roche, J., & Caton, S. (2018). Predicting the Price of Bitcoin Using Machine Learning. 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP) (pp. 339-343). Cambridge: IEEE.
  11. Qiu, J., Wang, B., & Zhou, C. (2020). Forecasting Stock Prices with Long-Short Term Memory Neural Network Based on Attention Mechanism. PLOS ONE 15(1): e0227222, 2-3. https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0227222.
  12. Sriwiji, R., & Primandari, A. H. (2019). An Empirical Study in Forecasting Bitcoin Price Using Bayesian Regularization Neural Network. Proceedings of the 1st International Conference on Statistics and Analytics (pp. 350-361). Bogor: EAI.
  13. Wu, C.-H., Lu, C.-C., Ma, Y.-F., & Lu, R.-S. (2018). A New Forecasting Framework for Bitcoin Price with LSTM. EEE International Conference on Data Mining Workshops (ICDMW) (pp. 168-175). Singapore: IEEE.
  14. Zheng, J., Xu, C., Zhang, Z., & Li, X. (2017). Electric Load Forecasting in Smart Grid Using Long-Short-Term-Memory based Recurrent Neural Network. IEEE, 1-6. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7926112&isnumber=7926061.