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

https://doi.org/10.22146/ijccs.80776

Risna Sari(1), Kusrini Kusrini(2*), Tonny Hidayat(3), Theofanis Orphanoudakis(4)

(1) Dept. of Information Technology, Universitas Amikom Yogyakarta
(2) Dept. of Information Technology, Universitas Amikom Yogyakarta
(3) Dept. of Information Technology, Universitas Amikom Yogyakarta
(4) School of Science and Technology, Hellenic Open University, Patras
(*) Corresponding Author

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.


Keywords


Cryptocurrency; Prediction; Long Short-term Memory (LSTM); Short-term data

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References

[1] M. M. Patel, S. Tanwar, R. Gupta, and N. Kumar, “A Deep Learning-based Cryptocurrency Price Prediction Scheme for Financial Institutions,” J. Inf. Secure. Appl., vol. 55, no. August, p. 102583, 2020, doi: 10.1016/j.jisa.2020.102583.

[2] S. Tanwar, N. P. Patel, S. N. Patel, J. R. Patel, G. Sharma, and I. E. Davidson, “Deep Learning-Based Cryptocurrency Price Prediction Scheme with Inter-Dependent Relations,” IEEE Access, vol. 9, pp. 138633–138646, 2021, doi: 10.1109/ACCESS.2021.3117848.

[3] Capfemini, “Non-cash payments volume – World Payments Report.” https://worldpaymentsreport.com/non-cash-payments-volume-2/ (accessed September 29, 2022).

[4] S. Nakamoto, “Bitcoin: A Peer-to-Peer Electronic Cash System,” August 31, 2009. https://git.dhimmel.com/bitcoin-whitepaper/ (accessed September 29, 2022).

[5] P. N. Sureshbhai, P. Bhattacharya, and S. Tanwar, “KaRuNa: A blockchain-based sentiment analysis framework for fraud cryptocurrency schemes,” 2020 IEEE Int. Conf. Commun. Work. ICC Work. 2020 - Proc., pp. 0–5, 2020, doi: 10.1109/ICCWorkshops49005.2020.9145151.

[6] A. M. Khedr, I. Arif, P. V. Pravija Raj, M. El-Bannany, S. M. Alhashmi, and M. Sreedharan, “Cryptocurrency price prediction using traditional statistical and machine-learning techniques: A survey,” Intell. Syst. Accounting, Financ. Manag., vol. 28, no. 1, pp. 3–34, 2021, doi: 10.1002/isaf.1488.

[7] R. Böhme, N. Christin, B. Edelman, and T. Moore, “Bitcoin: Economics, Technology, and Governance,” vol. 29, no. 2, pp. 213–238, 2015.

[8] S. McNally, J. Roche, and S. Caton, “Predicting the Price of Bitcoin Using Machine Learning,” Proc. - 26th Euromicro Int. Conf. Parallel, Distrib. Network-Based Process. PDP 2018, pp. 339–343, 2018, doi: 10.1109/PDP2018.2018.00060.

[9] M. Rizwan, S. Narejo, and M. Javed, “Bitcoin price prediction using Deep Learning Algorithm,” MACS 2019 - 13th Int. Conf. Math. Actuar. Sci. Comput. Sci. Stat. Proc., pp. 1–7, 2019, doi: 10.1109/MACS48846.2019.9024772.

[10] M. Poongodi et al., “Prediction of the price of Ethereum blockchain cryptocurrency in an industrial finance system,” Comput. Electr. Eng., vol. 81, p. 106527, 2020, doi: 10.1016/j.compeleceng.2019.106527.

[11] M. Ali and S. Shatabda, “A data selection methodology to train linear regression model to predict bitcoin price,” 2020 2nd Int. Conf. Adv. Inf. Commun. Technol. ICAICT 2020, no. November 2020, pp. 330–335, 2020, doi: 10.1109/ICAICT51780.2020.9333525.

[12] A. Naghib-Moayed and R. Habibi, “Crypto-Currency Price Prediction with Decision Tree Based Regressions Approach,” J. Algorithms Comput., vol. 52, no. 2, pp. 29–40, 2020, [Online]. Available: http://jac.ut.ac.ir.

[13] A. Vo, Q. Nguyen, and C. Ock, “Sentiment Analysis of News for Effective Cryptocurrency Price Prediction,” vol. 5, no. 2, 2019, doi: 10.18178/ijke.2019.5.2.116.

[14] R. Parekh et al., “DL-GuesS: Deep Learning and Sentiment Analysis-Based Cryptocurrency Price Prediction,” IEEE Access, vol. 10, no. March, pp. 35398–35409, 2022, doi: 10.1109/ACCESS.2022.3163305.

[15] D. Setyo Dwitanto, “Analisis Runtun Waktu Untuk Meramalkan Jumlah Pasien Yang Berobat Di Puskesmas Blora Dengan Menggunakan Software Minitab 14,” UNIVERSITAS NEGERI SEMARANG, 2011.

[16] P. A. Raharja, “Prediksi Harga Ethereum Menggunakan Metode Vector Autoregressive,” J. Informatics, Inf. Syst. …, vol. 8106, pp. 71–79, 2021, [Online]. Available: http://journal.ittelkom-pwt.ac.id/index.php/inista/article/view/285.

[17] S. Saadah and H. Salsabila, “Prediksi Harga Bitcoin Menggunakan Metode Random Forest (Studi Kasus: Data Acak Pada Awal Masa Pandemic Covid-19),” J. Komput. Terap., vol. 7, no. Vol. 7 No. 1 (2021), pp. 24–32, 2021, doi: 10.35143/jkt.v7i1.4618.

[18] J. P. Fleischer, G. von Laszewski, C. Theran, and Y. J. P. Bautista, “Time Series Analysis of Cryptocurrency Prices Using Long Short-Term Memory,” Algorithms, vol. 15, no. 7, pp. 1–13, 2022, doi: 10.3390/a15070230.

[19] Bloomsburg University of Pennsylvania Library, “Definition A Literature review Literature review,” Lit. Rev., no. November, pp. 33–37, 2015.

[20] Supripto, R. N. Rahmanita, and A. S. Kirana, “Teknik pre-processing dan classification dalam data science – Master of Industrial Enginering,” 2022. https://mie.binus.ac.id/2022/08/26/teknik-pre-processing-dan-classification-dalam-data-science/ (accessed December 14, 2022).

[21] M. Farryz Rizkilloh and S. Widiyanesti, “Prediksi Harga Cryptocurrency Menggunakan Algoritma Long Short Term Memory (LSTM),” Resti, vol. 1, no. 1, pp. 19–25, 2022.

[22] D. Haryadi, A. R. Hakim, D. M. U. Atmaja, and S. N. Yutia, “Implementation of Support Vector Regression for Polkadot Cryptocurrency Price Prediction,” Int. J. Informatics Vis., vol. 6, no. 1–2, pp. 201–207, 2022, doi: 10.30630/joiv.6.1-2.945.

[23] H. Puspita and dkk, “Pengantar Teknologi Informasi - Google Books,” Heri Utama, 2022. https://www.google.co.id/books/edition/Pengantar_Teknologi_Informasi/43h8EAAAQBAJ?hl=en&gbpv=1&dq=LSTM+adalah&pg=PA197&printsec=frontcover (accessed Oct. 15, 2022).

[24] F. Faturohman, B. Irawan, and C. Setianingsih, “Analisis Sentimen Pada Bpjs Kesehatan Menggunakan Recurrent Neural Network,” e-Proceeding Eng., vol. 7, no. 2, pp. 4545–4552, 2020.

[25] Y. Yu, “A Review of Recurrent Neural Networks: LSTM Celss and Network Architectures,” vol. 31, no. March, pp. 2709–2733, 2019, doi: 10.1162/NECO.

[26] K. Grace and Martin, “Measures of Model Fit for Linear Regression Models,” theanalysisfactor, 2013. https://www.theanalysisfactor.com/assessing-the-fit-of-regression-models/ (accessed December 14, 2022).

[27] I. Ghozali, “Aplikasi Analisis Multivariete IBM SPSS 23, Badan Penerbit Universitas Diponegoro, Semarang,” 2016. https://scholar.google.co.id/citations?view_op=view_citation&hl=id&user=K8g3CywAAAAJ&citation_for_view=K8g3CywAAAAJ:r0BpntZqJG4C (accessed Dec. 14, 2022).

[28] Harinaldi, Prinsip-prinsip Statistik untuk Teknik dan Sains Fantastic Adventure : September 1939 Notes from a Literal Life Dødelig ideal Lullabies for Little Criminals Jesus Works Here : Leading Christians in Business Talk About How You Can Walk With Christ Through, no. September 1939. 2005.

[29] Y. Andrianto, “The Effect of Cryptocurrency on Investment Portfolio Effectiveness,” J. Financ. Account., vol. 5, no. 6, p. 229, 2017, doi: 10.11648/j.jfa.20170506.14.

[30] S. Karasu, A. Altan, Z. Saraç, and H. Ğ. Lu, “Prediction of Bitcoin prices with machine learning methods using time series data,” pp. 1–4, 2018.

[31] X. Chen, “A Multivariate Time Series Modeling and Forecasting Guide with Python Machine Learning Client for SAP HANA,” 2021. https://blogs.sap.com/2021/05/06/a-multivariate-time-series-modeling-and-forecasting-guide-with-python-machine-learning-client-for-sap-hana/ (accessed December 14, 2022).

[32] S. Zahara and Sugianto, “Peramalan Data Indeks Harga Konsumen Berbasis Time Series Multivariate Menggunakan Deep Learning,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 1, pp. 24–30, 2021, doi: 10.29207/resti.v5i1.2562.

[33] D. A. Lusia and A. Ambarwati, “Perbandingan Peramalan Univariat Dan Multivariat Arima Pada Indeks Harga Saham Gabungan,” J. Stat. Univ. Muhammadiyah Semarang, vol. 6, no. 2, 2018, [Online]. Available: https://jurnal.unimus.ac.id/index.php/statistik/article/view/4311.



DOI: https://doi.org/10.22146/ijccs.80776

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