SavedAdd your journal

Journals / Inspiring Technologies and Innovations / 2022 / Cilt: 1 Sayı: 2

Daily Digital Currency Values Estimation Using Artificial Intelligence Techniques

Ahmet Tabanlıoğlu(KASTAMONU ÜNİVERSİTESİ, MESLEK YÜKSEKOKULU)0000-0002-3876-196X
Abdülkadir Gümüşçü(HARRAN ÜNİVERSİTESİ)0000-0002-5948-595X
Pages
55–62
Publication Date
2022-12-30
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Conclusion: Considering the results obtained in the study, it was concluded that the estimation of Bitcoin closing values can be made daily using machine learning methods.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Aim: Recently, with the rapid rise in crypto money prices, Bitcoin has begun to be seen as an investment tool.
📊
Key Findings & Data:While creating the data sets, 300 days of data were used.
💡
Conclusion & Impact:Conclusion: Considering the results obtained in the study, it was concluded that the estimation of Bitcoin closing values can be made daily using machine learning methods.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Crypto-currency · Machine learning · Artificial neural networks

Abstract

Aim: Recently, with the rapid rise in crypto money prices, Bitcoin has begun to be seen as an investment tool. Because of this trend, predictions in the crypto money market gain importance. For this reason, in this study, a machine learning model was developed that can make daily predictions for bitcoin, the most important currency in the cryptocurrency market. Design & Methodology: An artificial neural network was used to make daily predictions for Bitcoin and the data set was designed with values from the coinmarketcap site. The next day's close price is estimated by using the open, high, low, volume, marketcap feature from this site. Originality: In this study, unlike other studies, the closing price of the next day was tried to be estimated. Thus, a model has been developed that makes a value estimation that the investor will need. While creating the data sets, 300 days of data were used. In addition, considering the changes in the bitcoin market, 3 different data sets were created as easy, moderate and hard. Findings: In the study, 0.9949, 0.9908 and 0.9503 R values were obtained in the test data sets of easy, moderate and hard difficulty levels, respectively. 70% of the data set was used for training. 15% of the data set was used to test the success of the model. The remaining samples were used for validation. Conclusion: Considering the results obtained in the study, it was concluded that the estimation of Bitcoin closing values can be made daily using machine learning methods. In addition, it has been observed that there is a serious decrease in success rates on days when the price changes are too much.

🔖

Cite This Article

Tabanlıoğlu, A., Gümüşçü, A. (2022). Daily Digital Currency Values Estimation Using Artificial Intelligence Techniques. Inspiring Technologies and Innovations, Cilt: 1 Sayı: 2, 55–62. https://ajindex.com/articles/1616295

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (20)

  1. Akyildirim, E., Goncu, A., & Sensoy, A. (2020). Prediction of cryptocurrency returns using machine learning. Annals of Operations Research, 297(1-2), 3–36. https://doi.org/10.1007/s10479-020-03575-y
  2. Alessandretti, L., ElBahrawy, A., Aiello, L. M., & Baronchelli, A. (2018). Anticipating cryptocurrency prices using machine learning. Complexity, 2018, 1–16. https://doi.org/10.1155/2018/8983590
  3. Altan, A., Karasu, S., & Bekiros, S. (2019). Digital currency forecasting with chaotic meta-heuristic bio-inspired Signal Processing Techniques. Chaos, Solitons & Fractals, 126, 325–336. https://doi.org/10.1016/j.chaos.2019.07.011
  4. Altan, G., & Demirci, S. (2022). Makine öğrenmesi Ile Nakit Akış Tablosu üzerinden Kredi Skorlaması: XGBoost Yaklaşımı. Journal of Economic Policy Researches / İktisat Politikası Araştırmaları Dergisi, 9(2), 397–424. https://doi.org/10.26650/jepr1114842
  5. Barkatullah, J., & Hanke, T. (2015). Goldstrike 1: Cointerra's first-generation cryptocurrency mining processor for Bitcoin. IEEE Micro, 35(2), 68–76. https://doi.org/10.1109/mm.2015.13
  6. Burniston, J. D. (1994). A neural network. University of Nottingham.
  7. Chen, Z., Li, C., & Sun, W. (2020). Bitcoin price prediction using machine learning: An approach to sample dimension engineering. Journal of Computational and Applied Mathematics, 365, 112395. https://doi.org/10.1016/j.cam.2019.112395
  8. Chowdhury, R., Rahman, M. A., Rahman, M. S., & Mahdy, M. R. C. (2020). An approach to predict and forecast the price of constituents and index of cryptocurrency using Machine Learning. Physica A: Statistical Mechanics and Its Applications, 551, 124569. https://doi.org/10.1016/j.physa.2020.124569
  9. Cryptocurrency prices, charts and market capitalizations. CoinMarketCap. (2022). Retrieved October 28, 2022, from https://coinmarketcap.com/
  10. Demirci, M. Y., Beşli, N., & Gümüşçü, A. (2021). Efficient deep feature extraction and classification for identifying defective photovoltaic module cells in electroluminescence images. Expert Systems with Applications, 175, 114810. https://doi.org/10.1016/j.eswa.2021.114810
  11. Gerger, M., & Gümüşçü, A. (2022). Diagnosis of Parkinson's Disease Using Spiral Test Based on Pattern Recognition. Romanian Journal of Information Science and Technology, 25(1), 100–113.
  12. Gumuscu, A., Karadag, K., Tenekeci, M. E., & Aydilek, I. B. (2017). Genetic algorithm based feature selection on diagnosis of parkinson disease via vocal analysis. 2017 25th Signal Processing and Communications Applications Conference (SIU). https://doi.org/10.1109/siu.2017.7960384
  13. Gümüşçü, A., & Tenekeci, M. E. (2018). Spermiogram Görüntülerinden Hareket Belirleme yöntemleri Ile Aktif sperm Sayısının Tahmini. Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 2018(18-2). https://doi.org/10.17341/gazimmfd.460524
  14. Gümüşçü, A., Tenekeci, M. E., & Bilgili, A. V. (2020). Estimation of wheat planting date using machine learning algorithms based on available climate data. Sustainable Computing: Informatics and Systems, 28, 100308. https://doi.org/10.1016/j.suscom.2019.01.010
  15. Gümüşçü, A., Tenekeci, M. E., & Tabanlıoğlu, A. (2018). The shortest path detection for unmanned aerial vehicles via genetic algorithm on aerial imaging of agricultural lands. International Advanced Researches and Engineering Journal, 2(3), 315–319.
  16. Hitam, N. A., & İsmail, A. R. (2018). Comparative performance of machine learning algorithms for cryptocurrency forecasting. Ind. J. Electr. Eng. Comput. Sci, 11(3), 1121–1128.
  17. Keller, J. M., Gray, M. R., & Givens, J. A. (1985). A fuzzy k-nearest neighbor algorithm. IEEE Transactions on Systems, Man, and Cybernetics, SMC-15(4), 580–585. https://doi.org/10.1109/tsmc.1985.6313426
  18. Li, X., & Wang, C. A. (2017). The technology and economic determinants of cryptocurrency exchange rates: The case of bitcoin. Decision Support Systems, 95, 49–60. https://doi.org/10.1016/j.dss.2016.12.001
  19. M., P., Sharma, A., V., V., Bhardwaj, V., Sharma, A. P., Iqbal, R., & Kumar, R. (2020). Prediction of the price of Ethereum blockchain cryptocurrency in an industrial finance system. Computers & Electrical Engineering, 81, 106527. https://doi.org/10.1016/j.compeleceng.2019.106527
  20. Zhang, W., Wang, P., Li, X., & Shen, D. (2018). Some stylized facts of the cryptocurrency market. Applied Economics, 50(55), 5950–5965. https://doi.org/10.1080/00036846.2018.1488076