Araştırma Makalesi
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Makine Öğrenimi Algoritması ile Monero Fiyatlarının Tahmin Edilmesi

Yıl 2021, Cilt 16, Sayı 3, 651 - 663, 01.12.2021
https://doi.org/10.17153/oguiibf.932839

Öz

Literatürde birçok araştırmacının farklı kripto para birimlerinin değerlerini tahmin etmeye çalıştıkları gözlemlenmiş, fakat Monero fiyat eğilimlerini analiz eden çok az çalışmaya rastlanmıştır. Monero gizlilik özellikleri açısından kripto paralar arasında ilk sırada yer almakta ve talebinin gelecekte artacağı beklenmektedir. Bu makale, Monero fiyatlarını ve trendlerini tahmin etmek için PATSOS modelini kullanan ilk çalışma olarak sınıflandırılabilir. Bulgulara göre PATSOS modeli, çok düşük bir hata oranıyla gelecekteki Monero fiyatlarını doğru bir şekilde tahmin etmektedir. Ayrıca, yatırımcılar PATSOS mekanizması tarafından üretilen tutarlı "al" ve "sat" sinyallerini kullanarak piyasa oynaklığına direnebilir ve büyük kayıpları önleyebilirler.

Kaynakça

  • Aggarwal, D.; Chandrasekaran, S.; Annamalai, B. (2020), “A Complete Empirical Ensemble Mode Decomposition and Support Vector Machine-Based Approach to Predict Bitcoin Prices”, Journal of Behavioral and Experimental Finance, Vol. 27, No. 2020: 1-12.
  • Akyildirim, E.; Goncu, A.; Sensoy, A. (2021), “Prediction of Cryptocurrency Returns Using Machine Learning”, Annals of Operations Research, Vol. 297, No. 1: 3-36.
  • Altan, A.; Karasu, S.; Bekiros, S. (2019), “Digital Currency Forecasting with Chaotic Meta-Heuristic Bio-Inspired Signal Processing Techniques”, Chaos, Solitons & Fractals, Vol. 126, No. 2019: 325-336.
  • Atsalakis, G. S.; Valavanis, K. P. (2009), "Forecasting Stock Market Short-Term Trends Using A Neuro-Fuzzy Based Methodology", Expert Systems with Applications, Vol. 36, No. 7: 10696–10707.
  • Atsalakis, G. S.; Frantzis, D.; Zopounidis, C. (2015), "Energy’s Exports Forecasting by a Neuro-Fuzzy Controller", Energy Systems, Vol. 6, No. 2: 249–267.
  • Atsalakis, G. S. (2016), "Using Computational Intelligence to Forecast Carbon Prices", Applied Soft Computing Journal, Vol. 43, No. 2016: 107–116.
  • Atsalakis, G. S.; Protopapadakis, E. E.; Valavanis, K. P. (2016a), "Stock Trend Forecasting in Turbulent Market Periods Using Neuro-Fuzzy Systems", Operational Research, Vol. 16, No. 2: 245–269.
  • Atsalakis, G. S.; Frantzis, D.; Zopounidis, C. (2016b), "Commodities’ Price Trend Forecasting by A Neuro-Fuzzy Controller", Energy Systems, Vol. 7, No. 1: 73–102.
  • Atsalakis, G. S.; Atsalakis, I. G.; Pasiouras, F.; Zopounidis, C. (2019), "Bitcoin Price Forecasting with Neuro-Fuzzy Techniques", European Journal of Operational Research, Vol. 276, No. 2: 770–780.
  • Chen, W.; Xu, H.; Jia, L.; Gao, Y. (2021), "Machine Learning Model for Bitcoin Exchange Rate Prediction Using Economic and Technology Determinants", International Journal of Forecasting, Vol. 37, No. 1: 28-43.
  • 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, Vol. 551, No.2020: 1-17.
  • Fama, E. F. (1965), "The Behavior of Stock-Market Prices", The Journal of Business, Vol. 38 No. 1: 34–105.
  • Jay, P.; Kalariya, V.; Parmar, P.; Tanwar, S.; Kumar, N.; Alazab, M. (2020), "Stochastic Neural Networks for Cryptocurrency Price Prediction", IEEE Access, Vol. 8, No. 2020: 82804-82818.
  • Ji, S.; Kim, J.; Im, H. (2019), "A Comparative Study of Bitcoin Price Prediction Using Deep Learning", Mathematics, Vol. 7, No. 10: 898.
  • Karasu, S.; Altan, A.; Saraç, Z.; Hacıoğlu, R. (2018), "Prediction of Bitcoin Prices with Machine Learning Methods Using Time Series Data", https://ieeexplore.ieee.org/abstract/document/8404760, (Accessed: 03.05.2021).
  • Kristjanpoller, W.; Minutolo, M. C. (2018), "A Hybrid Volatility Forecasting Framework Integrating GARCH, Artificial Neural Network, Technical Analysis and Principal Components Analysis", Expert Systems with Applications, Vol. 109, No.2018: 1-11.
  • Lahmiri, S.; Bekiros, S. (2019), "Cryptocurrency Forecasting with Deep Learning Chaotic Neural Networks", Chaos, Solitons & Fractals, Vol. 118, No. 2019: 35-40.
  • Liu, M.; Li, G.; Li, J.; Zhu, X.; Yao, Y. (2020), "Forecasting the Price of Bitcoin Using Deep Learning", Finance Research Letters, Article in Press, 101755.
  • McNally, S.; Roche, J.; Caton, S. (2018), "Predicting the Price of Bitcoin Using Machine Learning", https://ieeexplore.ieee.org/abstract/document/8374483, (Accessed: 03.05.2021)
  • Nakamoto, S. (2009), "Bitcoin: Peer-to-Peer Electronic Cash System". https://nakamotoinstitute.org/bitcoin/, (Accessed: 01.05.2021).
  • Nakano, M.; Takahashi, A.; Takahashi, S. (2018), "Bitcoin Technical Trading with Artificial Neural Network", Physica A: Statistical Mechanics and its Applications, Vol. 510, No. 2018: 587-609.
  • Norgaard, M.; Ravnm, O.; Poulsen, N. K. (2003), Neural Networks for Modelling and Control Dynamic Systems, London: Springer.
  • Patel, M. M.; Tanwar, S.; Gupta, R.; Kumar, N. (2020), "A Deep Learning-Based Cryptocurrency Price Prediction Scheme for Financial Institutions", Journal of Information Security and Applications, Vol. 55, No. 2020: 1-12.
  • Pintelas, E.; Livieris, I. E.; Stavroyiannis, S.; Kotsilieris, T.; Pintelas, P. (2020), "Investigating the problem of cryptocurrency price prediction: a deep learning approach". In: Maglogiannis I., Iliadis L., Pimenidis E. (eds) Artificial Intelligence Applications and Innovations. AIAI 2020. IFIP Advances in Information and Communication Technology, Springer, vol 584: 99-110.
  • Poongodi, M.; Sharma, A.; Vijayakumar, 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, Vol. 81, No. 2020: 1-12.
  • Sun, X.; Liu, M.; Sima, Z. (2020), "A Novel Cryptocurrency Price Trend Forecasting Model Based on LightGBM", Finance Research Letters, Vol. 32, No. 2020: 1-6.
  • Urfalıoğlu, F.; Tanrıverdi, İ. (2018), "Anfis ve Regresyon Analizi ile Enflasyon Tahmini ve Karşılaştırması", Social Sciences Research Journal, Vol. 7, No. 3: 120–141.
  • Yücel, A.; Güneri, A. F. (2010), "Application of Adaptive Neuro Fuzzy Inference System to Supplier Selection Problem", Journal of Engineering and Natural Sciences, Vol. 28, No. 212: 224–234.
  • www.coinmarketcap.com, (Accessed 07.04.2021)

Forecasting Monero Prices with a Machine Learning Algorithm

Yıl 2021, Cilt 16, Sayı 3, 651 - 663, 01.12.2021
https://doi.org/10.17153/oguiibf.932839

Öz

Many researchers have attempted to forecast the values of different cryptocurrencies, but few studies analyzed the Monero price trends. Monero ranks first in terms of privacy features, and its demand is expected to grow in the future. This paper can be classified as the first to use the PATSOS model to forecast Monero prices and trends. According to the findings, the PATSOS model accurately forecasted future Monero prices with a very low error rate. Moreover, investors can withstand market volatility and avoid large losses by using the consistent "buy" and "sell" signals produced by the PATSOS mechanism.

Kaynakça

  • Aggarwal, D.; Chandrasekaran, S.; Annamalai, B. (2020), “A Complete Empirical Ensemble Mode Decomposition and Support Vector Machine-Based Approach to Predict Bitcoin Prices”, Journal of Behavioral and Experimental Finance, Vol. 27, No. 2020: 1-12.
  • Akyildirim, E.; Goncu, A.; Sensoy, A. (2021), “Prediction of Cryptocurrency Returns Using Machine Learning”, Annals of Operations Research, Vol. 297, No. 1: 3-36.
  • Altan, A.; Karasu, S.; Bekiros, S. (2019), “Digital Currency Forecasting with Chaotic Meta-Heuristic Bio-Inspired Signal Processing Techniques”, Chaos, Solitons & Fractals, Vol. 126, No. 2019: 325-336.
  • Atsalakis, G. S.; Valavanis, K. P. (2009), "Forecasting Stock Market Short-Term Trends Using A Neuro-Fuzzy Based Methodology", Expert Systems with Applications, Vol. 36, No. 7: 10696–10707.
  • Atsalakis, G. S.; Frantzis, D.; Zopounidis, C. (2015), "Energy’s Exports Forecasting by a Neuro-Fuzzy Controller", Energy Systems, Vol. 6, No. 2: 249–267.
  • Atsalakis, G. S. (2016), "Using Computational Intelligence to Forecast Carbon Prices", Applied Soft Computing Journal, Vol. 43, No. 2016: 107–116.
  • Atsalakis, G. S.; Protopapadakis, E. E.; Valavanis, K. P. (2016a), "Stock Trend Forecasting in Turbulent Market Periods Using Neuro-Fuzzy Systems", Operational Research, Vol. 16, No. 2: 245–269.
  • Atsalakis, G. S.; Frantzis, D.; Zopounidis, C. (2016b), "Commodities’ Price Trend Forecasting by A Neuro-Fuzzy Controller", Energy Systems, Vol. 7, No. 1: 73–102.
  • Atsalakis, G. S.; Atsalakis, I. G.; Pasiouras, F.; Zopounidis, C. (2019), "Bitcoin Price Forecasting with Neuro-Fuzzy Techniques", European Journal of Operational Research, Vol. 276, No. 2: 770–780.
  • Chen, W.; Xu, H.; Jia, L.; Gao, Y. (2021), "Machine Learning Model for Bitcoin Exchange Rate Prediction Using Economic and Technology Determinants", International Journal of Forecasting, Vol. 37, No. 1: 28-43.
  • 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, Vol. 551, No.2020: 1-17.
  • Fama, E. F. (1965), "The Behavior of Stock-Market Prices", The Journal of Business, Vol. 38 No. 1: 34–105.
  • Jay, P.; Kalariya, V.; Parmar, P.; Tanwar, S.; Kumar, N.; Alazab, M. (2020), "Stochastic Neural Networks for Cryptocurrency Price Prediction", IEEE Access, Vol. 8, No. 2020: 82804-82818.
  • Ji, S.; Kim, J.; Im, H. (2019), "A Comparative Study of Bitcoin Price Prediction Using Deep Learning", Mathematics, Vol. 7, No. 10: 898.
  • Karasu, S.; Altan, A.; Saraç, Z.; Hacıoğlu, R. (2018), "Prediction of Bitcoin Prices with Machine Learning Methods Using Time Series Data", https://ieeexplore.ieee.org/abstract/document/8404760, (Accessed: 03.05.2021).
  • Kristjanpoller, W.; Minutolo, M. C. (2018), "A Hybrid Volatility Forecasting Framework Integrating GARCH, Artificial Neural Network, Technical Analysis and Principal Components Analysis", Expert Systems with Applications, Vol. 109, No.2018: 1-11.
  • Lahmiri, S.; Bekiros, S. (2019), "Cryptocurrency Forecasting with Deep Learning Chaotic Neural Networks", Chaos, Solitons & Fractals, Vol. 118, No. 2019: 35-40.
  • Liu, M.; Li, G.; Li, J.; Zhu, X.; Yao, Y. (2020), "Forecasting the Price of Bitcoin Using Deep Learning", Finance Research Letters, Article in Press, 101755.
  • McNally, S.; Roche, J.; Caton, S. (2018), "Predicting the Price of Bitcoin Using Machine Learning", https://ieeexplore.ieee.org/abstract/document/8374483, (Accessed: 03.05.2021)
  • Nakamoto, S. (2009), "Bitcoin: Peer-to-Peer Electronic Cash System". https://nakamotoinstitute.org/bitcoin/, (Accessed: 01.05.2021).
  • Nakano, M.; Takahashi, A.; Takahashi, S. (2018), "Bitcoin Technical Trading with Artificial Neural Network", Physica A: Statistical Mechanics and its Applications, Vol. 510, No. 2018: 587-609.
  • Norgaard, M.; Ravnm, O.; Poulsen, N. K. (2003), Neural Networks for Modelling and Control Dynamic Systems, London: Springer.
  • Patel, M. M.; Tanwar, S.; Gupta, R.; Kumar, N. (2020), "A Deep Learning-Based Cryptocurrency Price Prediction Scheme for Financial Institutions", Journal of Information Security and Applications, Vol. 55, No. 2020: 1-12.
  • Pintelas, E.; Livieris, I. E.; Stavroyiannis, S.; Kotsilieris, T.; Pintelas, P. (2020), "Investigating the problem of cryptocurrency price prediction: a deep learning approach". In: Maglogiannis I., Iliadis L., Pimenidis E. (eds) Artificial Intelligence Applications and Innovations. AIAI 2020. IFIP Advances in Information and Communication Technology, Springer, vol 584: 99-110.
  • Poongodi, M.; Sharma, A.; Vijayakumar, 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, Vol. 81, No. 2020: 1-12.
  • Sun, X.; Liu, M.; Sima, Z. (2020), "A Novel Cryptocurrency Price Trend Forecasting Model Based on LightGBM", Finance Research Letters, Vol. 32, No. 2020: 1-6.
  • Urfalıoğlu, F.; Tanrıverdi, İ. (2018), "Anfis ve Regresyon Analizi ile Enflasyon Tahmini ve Karşılaştırması", Social Sciences Research Journal, Vol. 7, No. 3: 120–141.
  • Yücel, A.; Güneri, A. F. (2010), "Application of Adaptive Neuro Fuzzy Inference System to Supplier Selection Problem", Journal of Engineering and Natural Sciences, Vol. 28, No. 212: 224–234.
  • www.coinmarketcap.com, (Accessed 07.04.2021)

Ayrıntılar

Birincil Dil İngilizce
Konular Sosyal
Bölüm Makaleler
Yazarlar

Zeliha CAN ERGÜN (Sorumlu Yazar)
ADNAN MENDERES ÜNİVERSİTESİ
0000-0003-3357-9859
Türkiye


Busra KUTLU KARABIYIK
ADNAN MENDERES ÜNİVERSİTESİ
0000-0002-6691-2921
Türkiye

Yayımlanma Tarihi 1 Aralık 2021
Başvuru Tarihi 4 Mayıs 2021
Kabul Tarihi 1 Haziran 2021
Yayınlandığı Sayı Yıl 2021, Cilt 16, Sayı 3

Kaynak Göster

APA Can Ergün, Z. & Kutlu Karabıyık, B. (2021). Forecasting Monero Prices with a Machine Learning Algorithm . Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi , 16 (3) , 651-663 . DOI: 10.17153/oguiibf.932839