Araştırma Makalesi
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ANFIS MODELİ İLE BITCOIN FİYAT TAHMİNİ

Yıl 2021, Cilt 11, Sayı 22, 295 - 315, 29.11.2021
https://doi.org/10.53092/duiibfd.970900

Öz

Son zamanlarda piyasa değeri en yükseğe ulaşan Bitcoin, kripto para piyasasında büyük önem kazanmıştır. Bu yüzden, yatırımcılar ve araştırmacılar, Bitcoin fiyatlarını etkileyen faktörleri ve bunların tahmin edilebilir olup olmadığını bulmaya yönelik çalışmalar yürütmektedirler. Fakat literatürde, makine öğrenimi modellerini kullanarak Bitcoin fiyatlarını tahmin etmek için en etkili ekonomik ve teknik değişkenleri tanımlayan sınırlı sayıda araştırma bulunmaktadır. Bu nedenle bu çalışmada, çeşitli ekonomik ve teknik faktörler kullanılarak Bitcoin fiyatlarının ANFIS modeli ile 01.05.2013-26.02.2021 tarihleri arasında tahmin edilmesi amaçlanmaktadır. Bulgular, ANFIS modelinin gerçek verilerle uyumlu, doğru ve tutarlı tahmin sonuçları ürettiğini göstermektedir. Sonuç olarak, gelecekteki Bitcoin değerlerini tahmin ederek kar elde etmek isteyen yatırımcılar, bir tahmin aracı olarak ANFIS yaklaşımını tercih edebilirler.

Kaynakça

  • Adjei, F. (2019). Determinants of bitcoin expected returns. Journal of finance and economics, 7(1), 42-47.
  • 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, 27, 100335.
  • Akyildirim, E., Goncu, A., & Sensoy, A. (2021). Prediction of cryptocurrency returns using machine learning. Annals of operations research, 297(1), 3-36.
  • Albariqi, R., & Winarko, E. (2020, February). Prediction of bitcoin price change using neural networks [Conference presentation]. International Conference on Smart Technology and Applications (ICoSTA), 1-4.
  • 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.
  • Atsalakis, G. S., Atsalakis, I. G., Pasiouras, F., & Zopounidis, C. (2019). Bitcoin price forecasting with neuro-fuzzy techniques. European journal of operational research, 276(2), 770–780.
  • Bayramoğlu, T., Pabuççu, H., & Çelebi Boz, F. (2017). Türkiye için anfıs modeli ile birincil enerji talep tahmini. Ege akademik bakış, 17(3), 431–446.
  • Chen, T. H., Chen, M. Y., & Du, G. T. (2021). The determinants of bitcoin’s price: Utilization of GARCH and machine learning approaches. Computational Economics, 57(1), 267-280.
  • 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, 37(1), 28–43. https://doi.org/10.1016/j.ijforecast.2020.02.008
  • Ciaian, P., Rajcaniova, M., & Kancs, D. A. (2016). The economics of bitcoin price formation. Applied economics, 48(19), 1799-1815.
  • Dutta, A., Kumar, S., & Basu, M. (2020). A gated recurrent unit approach to bitcoin price prediction. Journal of risk and financial management, 13(2), 1–16. https://doi.org/10.3390/jrfm13020023.
  • Edwards, J. (2021). Bitcoin's Price History. In Investopedia. https://www.investopedia.com/articles/forex/121815/bitcoins-price-history.asp (Retrieved 12.06.2021).
  • Fortune Business Insight (2020), https://www.fortunebusinessinsights.com/industry-reports/cryptocurrency-market-100149 (Retrieved 31.05.2021).
  • Georgoula, I., Pournarakis, D., Bilanakos, C., Sotiropoulos, D., & Giaglis, G. M., Using Time-Series and Sentiment Analysis to Detect the Determinants of Bitcoin Prices (May 17, 2015). Available at SSRN: https://ssrn.com/abstract=2607167 (Retrieved 19.05.2021)
  • Guizani, S., & Nafti, I. K. (2019). The determinants of bitcoin price volatility: an investigation with ardl model. Procedia computer science, 164, 233-238.
  • Jang, J. R. (1993). Anfis : adaptive-network-based fuzzy inference system. Ieee transactions on systems, man, and cybernetics, 23(3), 665–685.
  • Jang, H., & Lee, J. (2017). An empirical study on modeling and prediction of bitcoin prices with bayesian neural networks based on blockchain information. Ieee access, 6, 5427–5437. https://doi.org/10.1109/ACCESS.2017.2779181
  • Jaquart, P., Dann, D., & Weinhardt, C. (2021). Short-term bitcoin market prediction via machine learning. The journal of finance and data science, 7, 45-66. https://doi.org/10.1016/j.jfds.2021.03.001
  • Jay, P., Kalariya, V., Parmar, P., Tanwar, S., Kumar, N., & Alazab, M. (2020). Stochastic neural networks for cryptocurrency price prediction. Ieee access, 8, 82804-82818.
  • Ji, S., Kim, J., & Im, H. (2019). A comparative study of bitcoin price prediction using deep learning. Mathematics, 7(10), 898.
  • Karasu, S., Altan, A., Saraç, Z., & Hacioğlu, R. (2018). Prediction of Bitcoin prices with machine learning methods using time series data [Conference Presentation]. 26th signal processing and communications applications conference (SIU), 1-4.
  • 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, 109, 1-11.
  • Kristoufek, L. (2013). Bitcoin meets google trends and wikipedia: quantifying the relationship between phenomena of the internet era. Scientific reports, 3(1), 1-7.
  • Kristoufek, L. (2015). What are the main drivers of the Bitcoin price? Evidence from wavelet coherence analysis. PloS one, 10(4), e0123923.
  • Lahmiri, S., & Bekiros, S. (2019). Cryptocurrency forecasting with deep learning chaotic neural networks. Chaos, solitons & fractals, 118, 35-40.
  • Lamothe-Fernández, P., Alaminos, D., Lamothe-López, P., & Fernández-Gámez, M. A. (2020). Deep learning methods for modeling bitcoin price prosper. Mathematics, 8(8), 1–13. https://doi.org/10.3390/MATH8081245
  • 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
  • Li, Y., & Dai, W. (2020). Bitcoin price forecasting method based on cnn‐lstm hybrid neural network model. The journal of engineering, 2020(13), 344–347. https://doi.org/10.1049/joe.2019.1203
  • Liu, M., Li, G., Li, J., Zhu, X., & Yao, Y. (2021). Forecasting the price of bitcoin using deep learning. Finance research letters, 40, 101755.
  • Loh, E. C., & Ismail, S. (2020). Emerging trend of transaction and investment: bitcoin price prediction using machine learning. International journal of advanced trends in computer science and engineering, 9(1.4), 100–104. https://doi.org/10.30534/ijatcse/2020/1591.42020
  • Loh, E. C., Ismail, S., Khamis, A., & Mustapha, A. (2020). Comparison of feedforward neural network with different training algorithms for bitcoin price forecasting. Asm science journal, 13, 1–7. https://doi.org/10.32802/asmscj.2020.sm26(1.5)
  • McNally, S., Roche, J., & Caton, S. (2018). Predicting the price of bitcoin using machine learning [Conference Presentation]. 26th euromicro international conference on parallel, distributed and network-based processing (PDP), 339-343.
  • Malik, S. (2020). Drivers of bitcoin prices: an empirical analysis of india. Journal of critical reviews, 7(14), 1252-1258.
  • Mallqui, D. C. A., & Fernandes, R. A. S. (2019). Predicting the direction, maximum, minimum and closing prices of daily bitcoin exchange rate using machine learning techniques. Applied soft computing journal, 75, 596–606. https://doi.org/10.1016/j.asoc.2018.11.038
  • Mudassir, M., Bennbaia, S., Unal, D., & Hammoudeh, M. (2020). Time-series forecasting of bitcoin prices using high-dimensional features: a machine learning approach. Neural computing and applications, 6, 1–15. https://doi.org/10.1007/s00521-020-05129-6
  • Nakamoto, S. (2009). Bitcoin: Peer-to-Peer Electronic Cash System. https://nakamotoinstitute.org/bitcoin/, (Retrieved 01.05.2021).
  • Nakano, M., Takahashi, A., & Takahashi, S. (2018). Bitcoin technical trading with artificial neural network. Physica a: statistical mechanics and its applications, 510, 587-609.
  • Panagiotidis, T., Stengos, T., & Vravosinos, O. (2018). Pn the determinants of bitcoin returns: a lasso approach. Finance research letters, 27, 235-240.
  • 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, 55, 102583.
  • Pintelas, E., Livieris, I. E., Stavroyiannis, S., Kotsilieris, T., & Pintelas, P. (2020). Investigating the problem of cryptocurrency price prediction: a deep learning approach [Conference Presentation]. IFIP International conference on artificial intelligence applications and innovations, 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, 81, 106527.
  • Poyser, O. (2019). Exploring the dynamics of bitcoin’s price: a bayesian structural time series approach. Eurasian economic review, 9(1), 29-60.
  • Sin, E., & Wang, L. (2017). Bitcoin price prediction using ensembles of neural networks [Conference Presentation]. 13th international conference on natural computation, fuzzy systems and knowledge discovery, 666–671.
  • Shen, D., Urquhart, A., & Wang, P. (2019). Does twitter predict bitcoin?. Economics letters, 174, 118-122.
  • Struga, K., & Qirici, O. (2018). Bitcoin price prediction with neural networks. Ceur workshop proceedings, 2280, 41–49.
  • Sovbetov, Y. (2018). Factors influencing cryptocurrency prices: evidence from bitcoin, ethereum, dash, litcoin, and monero. Journal of economics and financial analysis, 2(2), 1-27.
  • Urfalıoğlu, F., & Tanrıverdi, İ. (2018). Anfis ve regresyon analizi ile enflasyon tahmini ve karşılaştırması. Social sciences research journal, 7(3), 120–141.
  • Yavuz, U., Özen, Ü., Taş, K., & Çağlar, B. (2020). Yapay sinir ağları ile blockchain verilerine dayalı bitcoin fiyat tahmini. Journal of information systems and management research, 2(1), 1–9.
  • Yücel, A., & Güneri, A. F. (2010). Application of adaptive neuro fuzzy inference system to supplier selection problem. Journal of engineering and natural sciences, 28(212), 224–234.
  • www.coinmarketcap.com (Accessed: 12.06.2021).

FORECASTING BITCOIN PRICES WITH THE ANFIS MODEL

Yıl 2021, Cilt 11, Sayı 22, 295 - 315, 29.11.2021
https://doi.org/10.53092/duiibfd.970900

Öz

Recently, Bitcoin has gained great importance in the cryptocurrency market with the highest market capitalization. Investors and researchers have attempted to find out the drivers of Bitcoin prices and if they are predictable. However, there is only limited research in the literature that identifies the most effective economic and technical variables for predicting Bitcoin prices using machine learning models. Thus, in this study, the future Bitcoin prices utilizing several economic and technical factors using the ANFIS model are aimed to forecasted between 01.05.2013 - 26.02.2021 periods. The findings show that the ANFIS model produced accurate and consistent predicting results that are in line with the real data. As a result, investors who wish to make a profit by predicting future Bitcoin values might consider using the ANFIS approach as a forecasting tool.

Kaynakça

  • Adjei, F. (2019). Determinants of bitcoin expected returns. Journal of finance and economics, 7(1), 42-47.
  • 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, 27, 100335.
  • Akyildirim, E., Goncu, A., & Sensoy, A. (2021). Prediction of cryptocurrency returns using machine learning. Annals of operations research, 297(1), 3-36.
  • Albariqi, R., & Winarko, E. (2020, February). Prediction of bitcoin price change using neural networks [Conference presentation]. International Conference on Smart Technology and Applications (ICoSTA), 1-4.
  • 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.
  • Atsalakis, G. S., Atsalakis, I. G., Pasiouras, F., & Zopounidis, C. (2019). Bitcoin price forecasting with neuro-fuzzy techniques. European journal of operational research, 276(2), 770–780.
  • Bayramoğlu, T., Pabuççu, H., & Çelebi Boz, F. (2017). Türkiye için anfıs modeli ile birincil enerji talep tahmini. Ege akademik bakış, 17(3), 431–446.
  • Chen, T. H., Chen, M. Y., & Du, G. T. (2021). The determinants of bitcoin’s price: Utilization of GARCH and machine learning approaches. Computational Economics, 57(1), 267-280.
  • 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, 37(1), 28–43. https://doi.org/10.1016/j.ijforecast.2020.02.008
  • Ciaian, P., Rajcaniova, M., & Kancs, D. A. (2016). The economics of bitcoin price formation. Applied economics, 48(19), 1799-1815.
  • Dutta, A., Kumar, S., & Basu, M. (2020). A gated recurrent unit approach to bitcoin price prediction. Journal of risk and financial management, 13(2), 1–16. https://doi.org/10.3390/jrfm13020023.
  • Edwards, J. (2021). Bitcoin's Price History. In Investopedia. https://www.investopedia.com/articles/forex/121815/bitcoins-price-history.asp (Retrieved 12.06.2021).
  • Fortune Business Insight (2020), https://www.fortunebusinessinsights.com/industry-reports/cryptocurrency-market-100149 (Retrieved 31.05.2021).
  • Georgoula, I., Pournarakis, D., Bilanakos, C., Sotiropoulos, D., & Giaglis, G. M., Using Time-Series and Sentiment Analysis to Detect the Determinants of Bitcoin Prices (May 17, 2015). Available at SSRN: https://ssrn.com/abstract=2607167 (Retrieved 19.05.2021)
  • Guizani, S., & Nafti, I. K. (2019). The determinants of bitcoin price volatility: an investigation with ardl model. Procedia computer science, 164, 233-238.
  • Jang, J. R. (1993). Anfis : adaptive-network-based fuzzy inference system. Ieee transactions on systems, man, and cybernetics, 23(3), 665–685.
  • Jang, H., & Lee, J. (2017). An empirical study on modeling and prediction of bitcoin prices with bayesian neural networks based on blockchain information. Ieee access, 6, 5427–5437. https://doi.org/10.1109/ACCESS.2017.2779181
  • Jaquart, P., Dann, D., & Weinhardt, C. (2021). Short-term bitcoin market prediction via machine learning. The journal of finance and data science, 7, 45-66. https://doi.org/10.1016/j.jfds.2021.03.001
  • Jay, P., Kalariya, V., Parmar, P., Tanwar, S., Kumar, N., & Alazab, M. (2020). Stochastic neural networks for cryptocurrency price prediction. Ieee access, 8, 82804-82818.
  • Ji, S., Kim, J., & Im, H. (2019). A comparative study of bitcoin price prediction using deep learning. Mathematics, 7(10), 898.
  • Karasu, S., Altan, A., Saraç, Z., & Hacioğlu, R. (2018). Prediction of Bitcoin prices with machine learning methods using time series data [Conference Presentation]. 26th signal processing and communications applications conference (SIU), 1-4.
  • 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, 109, 1-11.
  • Kristoufek, L. (2013). Bitcoin meets google trends and wikipedia: quantifying the relationship between phenomena of the internet era. Scientific reports, 3(1), 1-7.
  • Kristoufek, L. (2015). What are the main drivers of the Bitcoin price? Evidence from wavelet coherence analysis. PloS one, 10(4), e0123923.
  • Lahmiri, S., & Bekiros, S. (2019). Cryptocurrency forecasting with deep learning chaotic neural networks. Chaos, solitons & fractals, 118, 35-40.
  • Lamothe-Fernández, P., Alaminos, D., Lamothe-López, P., & Fernández-Gámez, M. A. (2020). Deep learning methods for modeling bitcoin price prosper. Mathematics, 8(8), 1–13. https://doi.org/10.3390/MATH8081245
  • 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
  • Li, Y., & Dai, W. (2020). Bitcoin price forecasting method based on cnn‐lstm hybrid neural network model. The journal of engineering, 2020(13), 344–347. https://doi.org/10.1049/joe.2019.1203
  • Liu, M., Li, G., Li, J., Zhu, X., & Yao, Y. (2021). Forecasting the price of bitcoin using deep learning. Finance research letters, 40, 101755.
  • Loh, E. C., & Ismail, S. (2020). Emerging trend of transaction and investment: bitcoin price prediction using machine learning. International journal of advanced trends in computer science and engineering, 9(1.4), 100–104. https://doi.org/10.30534/ijatcse/2020/1591.42020
  • Loh, E. C., Ismail, S., Khamis, A., & Mustapha, A. (2020). Comparison of feedforward neural network with different training algorithms for bitcoin price forecasting. Asm science journal, 13, 1–7. https://doi.org/10.32802/asmscj.2020.sm26(1.5)
  • McNally, S., Roche, J., & Caton, S. (2018). Predicting the price of bitcoin using machine learning [Conference Presentation]. 26th euromicro international conference on parallel, distributed and network-based processing (PDP), 339-343.
  • Malik, S. (2020). Drivers of bitcoin prices: an empirical analysis of india. Journal of critical reviews, 7(14), 1252-1258.
  • Mallqui, D. C. A., & Fernandes, R. A. S. (2019). Predicting the direction, maximum, minimum and closing prices of daily bitcoin exchange rate using machine learning techniques. Applied soft computing journal, 75, 596–606. https://doi.org/10.1016/j.asoc.2018.11.038
  • Mudassir, M., Bennbaia, S., Unal, D., & Hammoudeh, M. (2020). Time-series forecasting of bitcoin prices using high-dimensional features: a machine learning approach. Neural computing and applications, 6, 1–15. https://doi.org/10.1007/s00521-020-05129-6
  • Nakamoto, S. (2009). Bitcoin: Peer-to-Peer Electronic Cash System. https://nakamotoinstitute.org/bitcoin/, (Retrieved 01.05.2021).
  • Nakano, M., Takahashi, A., & Takahashi, S. (2018). Bitcoin technical trading with artificial neural network. Physica a: statistical mechanics and its applications, 510, 587-609.
  • Panagiotidis, T., Stengos, T., & Vravosinos, O. (2018). Pn the determinants of bitcoin returns: a lasso approach. Finance research letters, 27, 235-240.
  • 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, 55, 102583.
  • Pintelas, E., Livieris, I. E., Stavroyiannis, S., Kotsilieris, T., & Pintelas, P. (2020). Investigating the problem of cryptocurrency price prediction: a deep learning approach [Conference Presentation]. IFIP International conference on artificial intelligence applications and innovations, 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, 81, 106527.
  • Poyser, O. (2019). Exploring the dynamics of bitcoin’s price: a bayesian structural time series approach. Eurasian economic review, 9(1), 29-60.
  • Sin, E., & Wang, L. (2017). Bitcoin price prediction using ensembles of neural networks [Conference Presentation]. 13th international conference on natural computation, fuzzy systems and knowledge discovery, 666–671.
  • Shen, D., Urquhart, A., & Wang, P. (2019). Does twitter predict bitcoin?. Economics letters, 174, 118-122.
  • Struga, K., & Qirici, O. (2018). Bitcoin price prediction with neural networks. Ceur workshop proceedings, 2280, 41–49.
  • Sovbetov, Y. (2018). Factors influencing cryptocurrency prices: evidence from bitcoin, ethereum, dash, litcoin, and monero. Journal of economics and financial analysis, 2(2), 1-27.
  • Urfalıoğlu, F., & Tanrıverdi, İ. (2018). Anfis ve regresyon analizi ile enflasyon tahmini ve karşılaştırması. Social sciences research journal, 7(3), 120–141.
  • Yavuz, U., Özen, Ü., Taş, K., & Çağlar, B. (2020). Yapay sinir ağları ile blockchain verilerine dayalı bitcoin fiyat tahmini. Journal of information systems and management research, 2(1), 1–9.
  • Yücel, A., & Güneri, A. F. (2010). Application of adaptive neuro fuzzy inference system to supplier selection problem. Journal of engineering and natural sciences, 28(212), 224–234.
  • www.coinmarketcap.com (Accessed: 12.06.2021).

Ayrıntılar

Birincil Dil İngilizce
Konular İktisat
Bölüm Araştırma Makalesi
Yazarlar

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


Zeliha CAN ERGÜN (Sorumlu Yazar)
Adnan Menderes Üniversitesi
0000-0003-3357-9859
Türkiye

Yayımlanma Tarihi 29 Kasım 2021
Başvuru Tarihi 13 Temmuz 2021
Kabul Tarihi 22 Ağustos 2021
Yayınlandığı Sayı Yıl 2021, Cilt 11, Sayı 22

Kaynak Göster

Bibtex @araştırma makalesi { duiibfd970900, journal = {Dicle Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi}, issn = {1309-4602}, eissn = {2587-0106}, address = {http://www.dicle.edu.tr/iibf-dergisi}, publisher = {Dicle Üniversitesi}, year = {2021}, volume = {11}, pages = {295 - 315}, doi = {10.53092/duiibfd.970900}, title = {FORECASTING BITCOIN PRICES WITH THE ANFIS MODEL}, key = {cite}, author = {Kutlu Karabıyık, Busra and Can Ergün, Zeliha} }
APA Kutlu Karabıyık, B. & Can Ergün, Z. (2021). FORECASTING BITCOIN PRICES WITH THE ANFIS MODEL . Dicle Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi , 11 (22) , 295-315 . DOI: 10.53092/duiibfd.970900
MLA Kutlu Karabıyık, B. , Can Ergün, Z. "FORECASTING BITCOIN PRICES WITH THE ANFIS MODEL" . Dicle Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi 11 (2021 ): 295-315 <https://dergipark.org.tr/tr/pub/duiibfd/issue/65939/970900>
Chicago Kutlu Karabıyık, B. , Can Ergün, Z. "FORECASTING BITCOIN PRICES WITH THE ANFIS MODEL". Dicle Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi 11 (2021 ): 295-315
RIS TY - JOUR T1 - FORECASTING BITCOIN PRICES WITH THE ANFIS MODEL AU - Busra Kutlu Karabıyık , Zeliha Can Ergün Y1 - 2021 PY - 2021 N1 - doi: 10.53092/duiibfd.970900 DO - 10.53092/duiibfd.970900 T2 - Dicle Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi JF - Journal JO - JOR SP - 295 EP - 315 VL - 11 IS - 22 SN - 1309-4602-2587-0106 M3 - doi: 10.53092/duiibfd.970900 UR - https://doi.org/10.53092/duiibfd.970900 Y2 - 2021 ER -
EndNote %0 Dicle Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi FORECASTING BITCOIN PRICES WITH THE ANFIS MODEL %A Busra Kutlu Karabıyık , Zeliha Can Ergün %T FORECASTING BITCOIN PRICES WITH THE ANFIS MODEL %D 2021 %J Dicle Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi %P 1309-4602-2587-0106 %V 11 %N 22 %R doi: 10.53092/duiibfd.970900 %U 10.53092/duiibfd.970900
ISNAD Kutlu Karabıyık, Busra , Can Ergün, Zeliha . "FORECASTING BITCOIN PRICES WITH THE ANFIS MODEL". Dicle Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi 11 / 22 (Kasım 2021): 295-315 . https://doi.org/10.53092/duiibfd.970900
AMA Kutlu Karabıyık B. , Can Ergün Z. FORECASTING BITCOIN PRICES WITH THE ANFIS MODEL. Dicle Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi. 2021; 11(22): 295-315.
Vancouver Kutlu Karabıyık B. , Can Ergün Z. FORECASTING BITCOIN PRICES WITH THE ANFIS MODEL. Dicle Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi. 2021; 11(22): 295-315.
IEEE B. Kutlu Karabıyık ve Z. Can Ergün , "FORECASTING BITCOIN PRICES WITH THE ANFIS MODEL", Dicle Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, c. 11, sayı. 22, ss. 295-315, Kas. 2021, doi:10.53092/duiibfd.970900

Dicle Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi
Dicle University, Journal of Economics and Administrative Sciences