Comparison of Support Vector Regression and Extreme Learning Machine Methods for Predicting Bitcoin Prices
DOI:
https://doi.org/10.30871/jaic.v9i5.10983Keywords:
Bitcoin, Support Vector Regression (SVR), Extreme Learning Machine (ELM), Predictio, ComparisonAbstract
Bitcoin can be used for transactions, mining, and investments. Transactions with Bitcoin are highly secure with the help of Bitcoin miner validation. Miners who validate transactions are rewarded with Bitcoins which then adds supply to the Bitcoin network. However, over time, these rewards will run out. The depletion of Bitcoin supply can affect the price of Bitcoin. In addition, investing in Bitcoin is very risky with the fluctuating price of Bitcoin. Therefore, it is necessary to predict the price. In this research, prediction is done using Support Vector Regression (SVR) and Extreme Learning Machine (ELM). The dataset for Bitcoin price (USD) comes from Yahoo Finance. The types of Bitcoin prices predicted are Open, High, Low, and Close prices. Across all series and both splits, ELM outperforms SVR. Under the 80/20 split, the average error of ELM is MAE 418.698 USD, RMSE 633.953 USD, R² of 0.987, versus SVR’s MAE 1061.449 USD, RMSE 1227.499 USD, R² of 0.955. A reduction of 60.6% (MAE) and 48.4% (RMSE). With the 60/40 split, ELM remains strong (MAE 550.783 USD, RMSE 850.656 USD, R² 0.989 while SVR deteriorates (MAE 1843.534 USD, RMSE 2093.542 USD, R² of 0.935, yielding 70.1% and 59.4% average reductions in MAE and RMSE, respectively. ELM consistently tracks both levels and day to day movements, with typical errors of only a few hundred dollars. These results indicate that ELM is the more reliable choice and is capable of capturing non-linearities for Bitcoin price prediction.
Downloads
References
[1] M. A. Fauzi, N. Paiman dan Z. Othman, “Bitcoin and Cryptocurrency: Challenges, Opportunities and Future Works,” The Journal of Asian Finance, Economics and Business, vol. 7, no. 8, pp. 695-704, 2020.
[2] R. Zhang, R. Xue dan L. Liu, “Security and Privacy on Blockchain,” ACM Computing Surveys,, vol. 52, no. 3, pp. 1-34, 2019.
[3] A. Alshamsi dan P. Andras, “User perception of Bitcoin usability and security across novice users,” International Journal of Human-Computer Studies, vol. 126, pp. 94-110, 2019.
[4] P. R. Cunha, P. Melo dan H. Sebastião, “From Bitcoin to Central Bank Digital Currencies: Making Sense of the Digital Money Revolution,” Future Internet, vol. 13, no. 7, pp. 1-19, 2021.
[5] J. M. Griffin dan A. Shams, “Is Bitcoin Really Untethered?,” The Journal of Finance, vol. 75, no. 4, pp. 1913-1964, 2020.
[6] D. Romano dan G. Schmid, “Beyond Bitcoin: Recent Trends and Perspectives in Distributed,” Cryptography, vol. 5, no. 4, pp. 1-45, 2021.
[7] L. Wang, Y. Yuan dan Y. Ding, “Analysis and Design of Identity Authentication for IoT Devices in the Blockchain Using Hashing and Digital Signature Algorithms,” International Journal of Distributed Sensor Networks, vol. 2023, no. 1, pp. 1-12, 2023.
[8] L. Zhu, B. Zheng, M. Shen, F. Gao, H. Li dan K. Shi, “Data Security and Privacy in Bitcoin System: A Survey,” Journal of Computer Science and Technology, vol. 35, no. 4, pp. 843-862, 2020.
[9] S. Anwar, S. Anayat, S. Butt, S. Butt dan M. Saad, “Generation Analysis of Blockchain Technology: Bitcoin and Ethereum,” International Journal of Information Engineering and Electronic Business (IJIEEB), vol. 12, no. 4, pp. 30-39, 2020.
[10] P. Nerurkar, D. Patel, Y. Busnel, R. Ludinard, S. Kumari dan M. K. Khan, “Dissecting bitcoin blockchain: Empirical Analysis of Bitcoin network (2009-2020),” Journal of Network and Computer Applications, vol. 177, p. 102940, 2021.
[11] K. Nicolas, Y. Wang, G. C. Giakos, B. Wei dan H. Shen, “Blockchain System Defensive Overview for Double-Spend and Selfish Mining Attacks: A Systematic Approach,” IEEE Access, vol. 9, pp. 3838-3857, 2021.
[12] K. Kang, “Cryptocurrency and double spending history: transactions with zero confirmation,” Economic Theory, vol. 75, p. 453–491, 2023.
[13] T. Rathee dan P. Singh, “Secure data sharing using Merkle hash digest based blockchain identity management,” Peer-to-Peer Networking and Applications, vol. 14, no. 2, pp. 3851-3864, 2021.
[14] C. Prybila, S. Schulte, C. Hochreiner dan I. Weber, “Runtime verification for business processes utilizing the Bitcoin blockchain,” Future Generation Computer Systems, vol. 107, pp. 816-831, 2020.
[15] E. Kruminis dan K. Navaie, “Game-Theoretic Analysis of an Exclusively Transaction-Fee Reward Blockchain System,” International Conference on Big Data Computing and Communications (BIGCOM), vol. 10, pp. 5002-5011, 2022.
[16] A. Singla, M. Singla dan M. Gupta, “Unpacking the Impact of Bitcoin Halving on the Crypto Market: Benefits and Limitations,” Scientific Journal of Metaverse and Blockchain Technologies, vol. 1, no. 1, pp. 43-50, 2023.
[17] A. L. Susanto, N. W. S. Saraswati, M. W. Adhiputra and I. D. M. K. Muku, "Prediksi Harga Bitcoin Menggunakan Metode Linear Regresion," KARMAPATI, vol. 13, no. 2, pp. 110-116, 2024.
[18] R. Fegiyanto, A. Hermawan and F. Ardiani, "Prediksi Harga Crypto dengan Algoritma Jaringan Saraf Tiruan," Jurnal Indonesia Manajemen Informatika dan Komunikasi, vol. 5, no. 3, pp. 2265-2275, 2024.
[19] W. Bakry, A. Rashid, S. Al-Mohamad dan N. El-Kanj, “Bitcoin and Portfolio Diversification: A Portfolio Optimization Approach,” Journal of Risk and Financial Management, vol. 14, no. 7, p. 282, 2021.
[20] N. Köse, H. Yildirim, E. Ünal dan B. Lin, “The Bitcoin price and Bitcoin price uncertainty: Evidence of Bitcoin price volatility,” The Journal of Futures Markets, vol. 44, no. 4, p. 673–695, 2024.
[21] X. Gao, W. Huang dan H. Wang, “Financial Twitter Sentiment On Bitcoin Return And High-Frequency,” Virtual Economics, vol. 4, no. 1, pp. 7-18, 2021.
[22] A. D. Gimenes, J. A. Colombo dan I. Yousaf, “Store of value or speculative investment? Market reaction to corporate announcements of cryptocurrency acquisition,” Financial Innovation, vol. 9, no. 123, 2023.
[23] Yahoo, “Bitcoin USD (BTC-USD),” [Online]. Available: https://finance.yahoo.com/quote/BTC-USD/history/.
[24] R. E. Cahyono, J. P. Sugiono dan S. Tjandra, “Analisis Kinerja Metode Support Vector Regression (SVR) dalam Memprediksi Indeks Harga Konsumen,” Jurnal Teknologi Informasi dan Multimedia, vol. 1, no. 2, pp. 106-116, 2019.
[25] P. Saha, P. Debnath dan P. Thomas, “Prediction of fresh and hardened properties of self-compacting concrete using support vector regression approach,” Neural Computing and Applications, vol. 32, p. 7995–8010, 2020.
[26] Z. M. Yaseen, S. O. Sulaiman, R. C. Deo dan K. Chau, “An enhanced extreme learning machine model for river flow forecasting: State-of-the-art, practical applications in water resource engineering area and future research direction,” Journal of Hydrology, vol. 569, pp. 387-408, 2019.
[27] R. K. Dash, T. N. Nguyen, K. Cengiz dan A. Sharma, “Fine-tuned support vector regression model for stock predictions,” Neural Computing and Applications, vol. 35, p. 23295–23309, 2023.
[28] J. Wang, S. Lu, S. Wang dan Y. Zhang, “A review on extreme learning machine,” Multimedia Tools and Applications, vol. 81, p. 41611–41660, 2022.
[29] J. S. Manoharan, “Study of Variants of Extreme Learning Machine (ELM) Brands and its Performance Measure on Classification Algorithm,” Journal of Soft Computing Paradigm, vol. 3, no. 2, pp. 83-95, 2021.
[30] R. Apriliyanti, N. Satyahadewi dan W. Andani, “Application Of Extreme Learning Machine Method On Stock Closing Price Forecasting PT Aneka Tambang (Persero) TBK,” BAREKENG: Jurnal Ilmu Matematika dan Terapan, vol. 17, no. 2, pp. 1057-1068, 2023.
[31] M. Steurer, R. J. Hill dan N. Pfeifer, “Metrics for evaluating the performance of machine learning based automated valuation models,” Journal of Property Research, vol. 38, no. 2, pp. 99-129, 2021.
[32] A. V. Tatachar, “Comparative Assessment of Regression Models Based On Model Evaluation Metrics,” International Research Journal of Engineering and Technology, vol. 8, no. 9, pp. 853-860, 2021.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Felix Ferdinand, Ryan Anthony , Tanjaya Jason Winata, Jason Sutanto, Richard Souwiko, Christian Fernando
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) ) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
