Research Article
BibTex RIS Cite

Comparing Forecasting Powers Of Traditional Methods And Learning Based Methods In Cryptocurrency Market: An Application On Bitcoin, Ethereum, Binance Coin And Monero

Year 2026, Volume: 18 Issue: 1, 37 - 50, 26.03.2026

Tahsin Galip Tekin , Sait Patır

https://doi.org/10.52791/aksarayiibd.1770977
https://izlik.org/JA54CH99SK

Abstract

In this study, it is aimed to compare quantitative forecasting methods (traditional and learning based) in cryptocurrency market. For his purpose the daily prices between 16 September 2017 – 15 September 2022 of Bitcoin, Ethereum, Binance Coin and Monero were analyzed with five different methods: ARIMA, exponential smoothing, artificial neural networks, RNN and LSTM.
In the results it is indicated that exponential smoothing method is the most successful method at forecasting daily prices. The method has high performance in forecasting BTC, ETH and BNB daily prices. But at forecasting daily XMR prices, artificial neural networks method was the most successful one.
The other point which was detected in this study is deep learning based methods made some unsuccessful forecasts. This is thought to be due to the fact that deep learning methods require more data. In future studies, using other quantitative methods (e.g. GRU, XGBoost, transformer models) on other cryptocurrencies will contribute to the literature.

Keywords

Traditional Methods Learning Based Methods Cryptocurrency Market Forecasting

References

  • Akaytay, A. (2010). Bağımsız denetimin etkinliğini arttırma aracı olarak yapay sinir ağları: Analitik bir inceleme, Sakarya University Institute of Social Sciences, Doctoral Thesis, Sakarya.
  • Al Guindy, M. (2021). Cryptocurrency price volatility and investor attention. International Review of Economics & Finance, 76, 556-570.
  • Alamsyah, A., Kusuma, G. N. W., & Ramadhani, D. P. (2024). A review on decentralized finance ecosystems. Future Internet, 16(3), 76.
  • Alessandretti, L., ElBahrawy, A., Aiello, L. M., & Baronchelli, A. (2018). Machine learning the cryptocurrency market. Complexity, 2018, 1-16.
  • Almeida, J., Tata, S., Moser, A. & Smit, V. (2015). Bitcoin prediction using ANN. Neural Networks, 7, 1-12.
  • Atsalakis, G. S., Atsalaki, I. G., Pasiouras, F., & Zopounidis, C. (2019). Bitcoin price forecasting with neuro-fuzzy techniques. European Journal of Operational Research, 276(2), 770-780.
  • Bariviera, A. F. (2017). The inefficiency of Bitcoin revisited: A dynamic approach. Economics Letters, 161, 1-4.
  • Barnes, P. (2018). Cryptocurrency and its susceptibility to speculative bubbles, manipulation, scams and fraud. Journal of Advanced Studies in Finance (JASF), 9(2 (18)), 60-77.
  • Baur, D. G., & Dimpfl, T. (2021). The volatility of Bitcoin and its role as a medium of exchange and a store of value. Empirical Economics, 61(5), 2663-2683.
  • Bengio, Y., Simard, P., & Frasconi, P. (1994). Learning long-term dependencies with gradient descent is difficult. IEEE transactions on neural networks, 5(2), 157-166.
  • Bouri, E., Molnár, P., Azzi, G., Roubaud, D., & Hagfors, L. I. (2017). On the hedge and safe haven properties of Bitcoin: Is it really more than a diversifier? Finance Research Letters, 20, 192–198.
  • Box, G. E. P., Jenkins, G. M., Reinsel, G. C. (1994). Time series analysis: Forecasting and control (3rd ed.). Practice – Hall.
  • Böhme, R., Christin, N., Edelman, B., & Moore, T. (2015). Bitcoin: Economics, technology, and governance. Journal of economic Perspectives, 29(2), 213-238.
  • Cheah, E.T. & Fry, J. (2015). Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin. Economics Letters, 130, 32-36.
  • Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078.
  • Chopra, S. & Meindl, P. (2016). Supply Chain Management, 6th Ed., London, Pearson.
  • Coinmarketcap. (2025). Retrieved on May 14, 2025 from https://coinmarketcap.com/.
  • Cong, L. W., & He, Z. (2019). Blockchain disruption and smart contracts. The Review of Financial Studies, 32(5), 1754-1797.
  • Corbet, S., Lucey, B., & Yarovaya, L. (2018). Datestamping the Bitcoin and Ethereum bubbles. Finance Research Letters, 26, 81-88.
  • Corbet, S., Lucey, B., Urquhart, A., & Yarovaya, L. (2019). Cryptocurrencies as a financial asset: A systematic analysis. International Review of Financial Analysis, 62, 182-199.
  • Dickey, D. A. & Fuller W. A. (1981). Likelihood radio statistics for autoregressive time series with a unit root. Econometrica: Journal of the Econometric Society, 49(4), 1057-1072.
  • Dongare, A. D., Kharde, R. R. & Kachare, A. D. (2012). Introduction to artificial neural network. International Journal of Engineering and Innovative Technology (IJEIT), 2(1), 189-194.
  • Dumitru, C., & Maria, V. (2013). Advantages and disadvantages of using neural networks for predictions. Ovidius University Annals Series Economic Sciences, 13(1), 444 – 449.
  • Dyhrberg, A. H. (2016). Bitcoin, gold and the dollar – A GARCH volatility analysis. Finance Research Letters, 16, 85–92.
  • Eigelshoven, F., Ullrich, A., & Parry, D. (2021). Cryptocurrency market manipulation - A Systematic Literature Review. In ICIS.
  • Enoksen, F.A., Landsnes, C.J., Lucivjanska, K. & Molnar P. (2020). Understanding risk of bubbles in cryptocurrencies. Journal of Economic Behaviour and Organization, 176, 129-144.
  • Fratric, P., Sileno, G., Klous, S., & ,Van Engers, T. (2022). Manipulation of the Bitcoin market: an agent-based study. Financial Innovation, 8(1), 60.
  • Gandal, N., Hamrick, J. T., Moore, T., & Oberman, T. (2018). Price manipulation in the Bitcoin ecosystem. Journal of Monetary Economics, 95, 86-96.
  • Gers, F. A., Schmidhuber, J., & Cummins, F. (2000). Learning to forget: Continual prediction with LSTM. Neural computation, 12(10), 2451-2471.
  • Gramlich, V., Guggenberger, T., Principato, M., Schellinger, B., & Urbach, N. (2023). A multivocal literature review of decentralized finance: Current knowledge and future research avenues. Electronic Markets, 33(1), 11.
  • Greff, K., Srivastava, R. K., Koutník, J., Steunebrink, B. R., & Schmidhuber, J. (2016). LSTM: A search space odyssey. IEEE transactions on neural networks and learning systems, 28(10), 2222-2232.
  • Guizani, S., & Nafti, I. K. (2019). The determinants of bitcoin price volatility: An investigation with ardl model. Procedia computer science, 164, 233-238.
  • Gümüş, E. (2024). Yapay sinir ağları ve derin öğrenme modeli kullanılarak USD/TRY döviz kurunun tahmin edilmesi. Anadolu Üniversitesi Sosyal Bilimler Dergisi, 24(2), 703-726.
  • Hitam, N. A. & Ismail, A. R. (2018). Comparative performance of machine learning algorithms for cryptocurrency forecasting. Indonesian Journal of Electrical Engineering and Computer Science, 11(3), 1121 – 1128.
  • Hochreiter, S., Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735
  • Holt, C. C. (1957). Forecasting seasonals and trends by exponentially weighted moving averages. Office of Naval Research Memorandum, Carnegie Institute of Technology.
  • Jenčová, S., Vašaničová, P., Košíková, M., & Miškufová, M. (2025). A Time Series Approach to Forecasting Financial Indicators in the Wholesale and Retail Trade. World, 6(1), 5.
  • İsabetli Fidan, İ., & Güz, T. (2022). The characteristics of cryptocurrency market volatility: Empirical study for five cryptocurrency. Alphanumeric Journal, 10(2), 69–84.
  • 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.
  • Jiang, S., Li, Y., Lu, Q., Hong, Y., Guan, D., Xiong, Y., & Wang, S. (2021). Policy assessments for the carbon emission flows and sustainability of Bitcoin blockchain operation in China. Nature communications, 12(1), 1938.
  • Jiang, Z., & Liang, J. (2017, September). Cryptocurrency portfolio management with deep reinforcement learning. In 2017 Intelligent systems conference (IntelliSys) 905-913.
  • Kaur, R., Uppal, M., Gupta, D., Juneja, S., Arafat, S. Y., Rashid, J., Kim, J. & Alroobaea, R. (2025). Development of a cryptocurrency price prediction model: leveraging GRU and LSTM for Bitcoin, Litecoin and Ethereum. PeerJ Computer Science, 11, e2675.
  • Kaynar, O. & Taştan, S. (2009). Zaman Serileri Tahmininde ARIMA – MLP Melez Modeli. Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, 23(3), 141-149.
  • Khedr, A. M., Arif, I., Pravija, R. P. V., El – Bannany, M., Alhashmi, S. M. & Sreedharan, M. (2021). Cryptocurrency price prediction using traditional statistical and machine – learning techniques: A survey. Intelligent Systems In Accounting, Finance and Management, 28(1), 3-34.
  • Kristoufek, L. (2013). Bitcoin meets Google trends and Wikipedia: Quantifying the relationship between phenomena of the Internet era. Scientific Reports, 3(1), 3415.
  • Kristoufek, L. (2015). What are the main drivers of the Bitcoin price? Evidence from wavelet coherence analysis. PloS one, 10(4), e0123923.
  • Krückeberg, S., & Scholz, P. (2020). Cryptocurrencies as an asset class. In Cryptofinance and mechanisms of exchange: The making of virtual currency 1-28. Cham: Springer International Publishing.
  • Latif, N., Selvam, J. D., Kapse, M., Sharma, V., & Mahajan, V. (2023). Comparative performance of LSTM and ARIMA for the short-term prediction of bitcoin prices. Australasian Accounting, Business and Finance Journal, 17(1), 256 – 276.
  • Lipton, Z. C., Berkowitz, J., & Elkan, C. (2015). A critical review of recurrent neural networks for sequence learning. arXiv preprint. arXiv:1506.00019.
  • Marella, V., Upreti, B., Merikivi, J., & Tuunainen, V. K. (2020). Understanding the creation of trust in cryptocurrencies: The case of Bitcoin. Electronic Markets, 30(2), 259-271.
  • McCulloch, W. S., Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, 5, 115-133.
  • Miers, I., Garman, C., Green, M., & Rubin, A. D. (2013). Zerocoin: Anonymous distributed e-cash from Bitcoin. IEEE Symposium on Security and Privacy, 397–411.
  • Mijwel, M. M. (2021). Artificial neural networks advantages and disadvantages. Mesopotamian Journal of Big Data, 2021, 29-31.
  • Mora, C., Rollins, R. L., Taladay, K., Kantar, M. B., Chock, M. K., Shimada, M., & Franklin, E. C. (2018). Bitcoin emissions alone could push global warming above 2 C. Nature Climate Change, 8(11), 931-933.
  • Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system.
  • Nakano, M., Takahashi, A. & Takahashi, S. (2018). Bitcoin Technical Trading with Artificial Neural Network. Physica A: Statistical Mechanics and Its Applications, 510, 587-609.
  • Neisse, R., Steri, G., & Nai-Fovino, I. (2017, August). A blockchain-based approach for data accountability and provenance tracking. In Proceedings of the 12th international conference on availability, reliability and security 1-10.
  • Phillips, P. C., Shi, S., & Yu, J. (2015). Testing for multiple bubbles: Historical episodes of exuberance and collapse in the S&P 500. International economic review, 56(4), 1043-1078.
  • Phillips, R. C., & Gorse, D. (2018). Cryptocurrency price drivers: Wavelet coherence analysis revisited. PloS one, 13(4), e0195200.
  • Rahardja, U., Chen, S. C., Lin, Y. C., Tsai, T. C., Aini, Q., Khan, A., Oganda, F.P., Dewi, E.R., Cho, Y.C., & Hsu, C. H. (2023). Evaluating the mediating mechanism of perceived trust and risk toward cryptocurrency: An empirical research. Sage Open, 13(4), 21582440231217854.
  • Rauchs, M., & Hileman, G. (2017). Global cryptocurrency benchmarking study (No. 201704-gcbs). Cambridge Centre for Alternative Finance, Cambridge Judge Business School, University of Cambridge.
  • Ray, D., & Hesthaven, J. S. (2018). An artificial neural network as a troubled-cell indicator. Journal of computational physics, 367, 166-191.
  • Rumelhart, D. E., Hinton, G. E., Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533-536. https://doi.org/10.1038/323533a0
  • Sak, H., Senior, A. W., & Beaufays, F. (2014, September). Long short-term memory recurrent neural network architectures for large scale acoustic modeling. In Interspeech 2014, 338-342.
  • Shah, A. S., Karabulut, M. A., Akhter, A. S., Mustari, N., Pathan, A. S. K., Rabie, K. M., & Shongwe, T. (2023). On the vital aspects and characteristics of cryptocurrency—A survey. Ieee Access, 11, 9451-9468.
  • Stoll, C., Klaaßen, L., & Gallersdörfer, U. (2019). The carbon footprint of bitcoin. Joule, 3(7), 1647-1661.
  • Tratar, L. F., & Strmčnik, E. (2016). The comparison of Holt–Winters method and Multiple regression method: A case study. Energy, 109, 266-276.
  • Tschorsch, F., & Scheuermann, B. (2016). Bitcoin and beyond: A technical survey on decentralized digital currencies. IEEE Communications Surveys & Tutorials, 18(3), 2084-2123.
  • Winters, P. R. (1960). Forecasting sales by exponentially weighted moving averages. Management Science, 6(3), 324–342. https://doi.org/10.1287/mnsc.6.3.324
  • Yousaf, H., Kappos, G., & Meiklejohn, S. (2019). Tracing transactions across cryptocurrency ledgers. In 28th USENIX Security Symposium (USENIX Security 19 837-850.
  • Zhang, G., Patuwo, B. E., & Hu, M. Y. (1998). Forecasting with artificial neural networks: The state of the art. International journal of forecasting, 14(1), 35-62.

Geleneksel Yöntemlerin Ve Öğrenme Temelli Yöntemlerin Tahmin Güçlerinin Kripto Para Piyasasında Karşılaştırılması: Bitcoin, Ethereum, Binance Coin Ve Monero Üzerine Bir Uygulama

Year 2026, Volume: 18 Issue: 1, 37 - 50, 26.03.2026

Tahsin Galip Tekin , Sait Patır

https://doi.org/10.52791/aksarayiibd.1770977
https://izlik.org/JA54CH99SK

Abstract

Bu çalışmada nicel tahmin yöntemlerinin (geleneksel ve öğrenme tabanlı) tahmin güçlerinin kripto para piyasasında karşılaştırılması amaçlanmıştır. Bu amaca yönelik olarak Bitcoin, Ethereum, Binance Coin ve Monero’nun 16 Eylül 2017 – 15 Eylül 2022 aralığındaki günlük fiyatları beş farklı yöntemle analiz edilmiştir: ARIMA, üstel düzeltme, yapay sinir ağları, RNN ve LSTM.
Sonuçlarda günlük fiyat tahminlemesinde en başarılı yöntemin üstel düzeltme yöntemi olduğu görülmüştür. Yöntem, BTC, ETH ve BNB günlük fiyat tahminlemesinde yüksek performansa sahiptir. Fakat XMR günlük fiyat tahminlemesinde yapay sinir ağları en başarılı olan yöntemdir.
Çalışmada tespit edilen diğer bir husus derin öğrenme tabanlı yöntemlerin bazı başarısız tahminlemeler yapmasıdır. Bu durumun derin öğrenme yöntemlerinin daha fazla veriye ihtiyaç duymasından kaynaklandığı düşünülmektedir. Gelecek çalışmalarda, başka nicel yöntemlerin (Örnek: GRU, XGBoost, transformer modeller) başka kripto para birimleri üzerinde kullanılması literature katkı sağlayacaktır.

Keywords

Geleneksel Yöntemler Öğrenme Tabanlı Yöntemler Kripto Para Piyasası Tahminleme

References

  • Akaytay, A. (2010). Bağımsız denetimin etkinliğini arttırma aracı olarak yapay sinir ağları: Analitik bir inceleme, Sakarya University Institute of Social Sciences, Doctoral Thesis, Sakarya.
  • Al Guindy, M. (2021). Cryptocurrency price volatility and investor attention. International Review of Economics & Finance, 76, 556-570.
  • Alamsyah, A., Kusuma, G. N. W., & Ramadhani, D. P. (2024). A review on decentralized finance ecosystems. Future Internet, 16(3), 76.
  • Alessandretti, L., ElBahrawy, A., Aiello, L. M., & Baronchelli, A. (2018). Machine learning the cryptocurrency market. Complexity, 2018, 1-16.
  • Almeida, J., Tata, S., Moser, A. & Smit, V. (2015). Bitcoin prediction using ANN. Neural Networks, 7, 1-12.
  • Atsalakis, G. S., Atsalaki, I. G., Pasiouras, F., & Zopounidis, C. (2019). Bitcoin price forecasting with neuro-fuzzy techniques. European Journal of Operational Research, 276(2), 770-780.
  • Bariviera, A. F. (2017). The inefficiency of Bitcoin revisited: A dynamic approach. Economics Letters, 161, 1-4.
  • Barnes, P. (2018). Cryptocurrency and its susceptibility to speculative bubbles, manipulation, scams and fraud. Journal of Advanced Studies in Finance (JASF), 9(2 (18)), 60-77.
  • Baur, D. G., & Dimpfl, T. (2021). The volatility of Bitcoin and its role as a medium of exchange and a store of value. Empirical Economics, 61(5), 2663-2683.
  • Bengio, Y., Simard, P., & Frasconi, P. (1994). Learning long-term dependencies with gradient descent is difficult. IEEE transactions on neural networks, 5(2), 157-166.
  • Bouri, E., Molnár, P., Azzi, G., Roubaud, D., & Hagfors, L. I. (2017). On the hedge and safe haven properties of Bitcoin: Is it really more than a diversifier? Finance Research Letters, 20, 192–198.
  • Box, G. E. P., Jenkins, G. M., Reinsel, G. C. (1994). Time series analysis: Forecasting and control (3rd ed.). Practice – Hall.
  • Böhme, R., Christin, N., Edelman, B., & Moore, T. (2015). Bitcoin: Economics, technology, and governance. Journal of economic Perspectives, 29(2), 213-238.
  • Cheah, E.T. & Fry, J. (2015). Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin. Economics Letters, 130, 32-36.
  • Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078.
  • Chopra, S. & Meindl, P. (2016). Supply Chain Management, 6th Ed., London, Pearson.
  • Coinmarketcap. (2025). Retrieved on May 14, 2025 from https://coinmarketcap.com/.
  • Cong, L. W., & He, Z. (2019). Blockchain disruption and smart contracts. The Review of Financial Studies, 32(5), 1754-1797.
  • Corbet, S., Lucey, B., & Yarovaya, L. (2018). Datestamping the Bitcoin and Ethereum bubbles. Finance Research Letters, 26, 81-88.
  • Corbet, S., Lucey, B., Urquhart, A., & Yarovaya, L. (2019). Cryptocurrencies as a financial asset: A systematic analysis. International Review of Financial Analysis, 62, 182-199.
  • Dickey, D. A. & Fuller W. A. (1981). Likelihood radio statistics for autoregressive time series with a unit root. Econometrica: Journal of the Econometric Society, 49(4), 1057-1072.
  • Dongare, A. D., Kharde, R. R. & Kachare, A. D. (2012). Introduction to artificial neural network. International Journal of Engineering and Innovative Technology (IJEIT), 2(1), 189-194.
  • Dumitru, C., & Maria, V. (2013). Advantages and disadvantages of using neural networks for predictions. Ovidius University Annals Series Economic Sciences, 13(1), 444 – 449.
  • Dyhrberg, A. H. (2016). Bitcoin, gold and the dollar – A GARCH volatility analysis. Finance Research Letters, 16, 85–92.
  • Eigelshoven, F., Ullrich, A., & Parry, D. (2021). Cryptocurrency market manipulation - A Systematic Literature Review. In ICIS.
  • Enoksen, F.A., Landsnes, C.J., Lucivjanska, K. & Molnar P. (2020). Understanding risk of bubbles in cryptocurrencies. Journal of Economic Behaviour and Organization, 176, 129-144.
  • Fratric, P., Sileno, G., Klous, S., & ,Van Engers, T. (2022). Manipulation of the Bitcoin market: an agent-based study. Financial Innovation, 8(1), 60.
  • Gandal, N., Hamrick, J. T., Moore, T., & Oberman, T. (2018). Price manipulation in the Bitcoin ecosystem. Journal of Monetary Economics, 95, 86-96.
  • Gers, F. A., Schmidhuber, J., & Cummins, F. (2000). Learning to forget: Continual prediction with LSTM. Neural computation, 12(10), 2451-2471.
  • Gramlich, V., Guggenberger, T., Principato, M., Schellinger, B., & Urbach, N. (2023). A multivocal literature review of decentralized finance: Current knowledge and future research avenues. Electronic Markets, 33(1), 11.
  • Greff, K., Srivastava, R. K., Koutník, J., Steunebrink, B. R., & Schmidhuber, J. (2016). LSTM: A search space odyssey. IEEE transactions on neural networks and learning systems, 28(10), 2222-2232.
  • Guizani, S., & Nafti, I. K. (2019). The determinants of bitcoin price volatility: An investigation with ardl model. Procedia computer science, 164, 233-238.
  • Gümüş, E. (2024). Yapay sinir ağları ve derin öğrenme modeli kullanılarak USD/TRY döviz kurunun tahmin edilmesi. Anadolu Üniversitesi Sosyal Bilimler Dergisi, 24(2), 703-726.
  • Hitam, N. A. & Ismail, A. R. (2018). Comparative performance of machine learning algorithms for cryptocurrency forecasting. Indonesian Journal of Electrical Engineering and Computer Science, 11(3), 1121 – 1128.
  • Hochreiter, S., Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735
  • Holt, C. C. (1957). Forecasting seasonals and trends by exponentially weighted moving averages. Office of Naval Research Memorandum, Carnegie Institute of Technology.
  • Jenčová, S., Vašaničová, P., Košíková, M., & Miškufová, M. (2025). A Time Series Approach to Forecasting Financial Indicators in the Wholesale and Retail Trade. World, 6(1), 5.
  • İsabetli Fidan, İ., & Güz, T. (2022). The characteristics of cryptocurrency market volatility: Empirical study for five cryptocurrency. Alphanumeric Journal, 10(2), 69–84.
  • 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.
  • Jiang, S., Li, Y., Lu, Q., Hong, Y., Guan, D., Xiong, Y., & Wang, S. (2021). Policy assessments for the carbon emission flows and sustainability of Bitcoin blockchain operation in China. Nature communications, 12(1), 1938.
  • Jiang, Z., & Liang, J. (2017, September). Cryptocurrency portfolio management with deep reinforcement learning. In 2017 Intelligent systems conference (IntelliSys) 905-913.
  • Kaur, R., Uppal, M., Gupta, D., Juneja, S., Arafat, S. Y., Rashid, J., Kim, J. & Alroobaea, R. (2025). Development of a cryptocurrency price prediction model: leveraging GRU and LSTM for Bitcoin, Litecoin and Ethereum. PeerJ Computer Science, 11, e2675.
  • Kaynar, O. & Taştan, S. (2009). Zaman Serileri Tahmininde ARIMA – MLP Melez Modeli. Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, 23(3), 141-149.
  • Khedr, A. M., Arif, I., Pravija, R. P. V., El – Bannany, M., Alhashmi, S. M. & Sreedharan, M. (2021). Cryptocurrency price prediction using traditional statistical and machine – learning techniques: A survey. Intelligent Systems In Accounting, Finance and Management, 28(1), 3-34.
  • Kristoufek, L. (2013). Bitcoin meets Google trends and Wikipedia: Quantifying the relationship between phenomena of the Internet era. Scientific Reports, 3(1), 3415.
  • Kristoufek, L. (2015). What are the main drivers of the Bitcoin price? Evidence from wavelet coherence analysis. PloS one, 10(4), e0123923.
  • Krückeberg, S., & Scholz, P. (2020). Cryptocurrencies as an asset class. In Cryptofinance and mechanisms of exchange: The making of virtual currency 1-28. Cham: Springer International Publishing.
  • Latif, N., Selvam, J. D., Kapse, M., Sharma, V., & Mahajan, V. (2023). Comparative performance of LSTM and ARIMA for the short-term prediction of bitcoin prices. Australasian Accounting, Business and Finance Journal, 17(1), 256 – 276.
  • Lipton, Z. C., Berkowitz, J., & Elkan, C. (2015). A critical review of recurrent neural networks for sequence learning. arXiv preprint. arXiv:1506.00019.
  • Marella, V., Upreti, B., Merikivi, J., & Tuunainen, V. K. (2020). Understanding the creation of trust in cryptocurrencies: The case of Bitcoin. Electronic Markets, 30(2), 259-271.
  • McCulloch, W. S., Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, 5, 115-133.
  • Miers, I., Garman, C., Green, M., & Rubin, A. D. (2013). Zerocoin: Anonymous distributed e-cash from Bitcoin. IEEE Symposium on Security and Privacy, 397–411.
  • Mijwel, M. M. (2021). Artificial neural networks advantages and disadvantages. Mesopotamian Journal of Big Data, 2021, 29-31.
  • Mora, C., Rollins, R. L., Taladay, K., Kantar, M. B., Chock, M. K., Shimada, M., & Franklin, E. C. (2018). Bitcoin emissions alone could push global warming above 2 C. Nature Climate Change, 8(11), 931-933.
  • Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system.
  • Nakano, M., Takahashi, A. & Takahashi, S. (2018). Bitcoin Technical Trading with Artificial Neural Network. Physica A: Statistical Mechanics and Its Applications, 510, 587-609.
  • Neisse, R., Steri, G., & Nai-Fovino, I. (2017, August). A blockchain-based approach for data accountability and provenance tracking. In Proceedings of the 12th international conference on availability, reliability and security 1-10.
  • Phillips, P. C., Shi, S., & Yu, J. (2015). Testing for multiple bubbles: Historical episodes of exuberance and collapse in the S&P 500. International economic review, 56(4), 1043-1078.
  • Phillips, R. C., & Gorse, D. (2018). Cryptocurrency price drivers: Wavelet coherence analysis revisited. PloS one, 13(4), e0195200.
  • Rahardja, U., Chen, S. C., Lin, Y. C., Tsai, T. C., Aini, Q., Khan, A., Oganda, F.P., Dewi, E.R., Cho, Y.C., & Hsu, C. H. (2023). Evaluating the mediating mechanism of perceived trust and risk toward cryptocurrency: An empirical research. Sage Open, 13(4), 21582440231217854.
  • Rauchs, M., & Hileman, G. (2017). Global cryptocurrency benchmarking study (No. 201704-gcbs). Cambridge Centre for Alternative Finance, Cambridge Judge Business School, University of Cambridge.
  • Ray, D., & Hesthaven, J. S. (2018). An artificial neural network as a troubled-cell indicator. Journal of computational physics, 367, 166-191.
  • Rumelhart, D. E., Hinton, G. E., Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533-536. https://doi.org/10.1038/323533a0
  • Sak, H., Senior, A. W., & Beaufays, F. (2014, September). Long short-term memory recurrent neural network architectures for large scale acoustic modeling. In Interspeech 2014, 338-342.
  • Shah, A. S., Karabulut, M. A., Akhter, A. S., Mustari, N., Pathan, A. S. K., Rabie, K. M., & Shongwe, T. (2023). On the vital aspects and characteristics of cryptocurrency—A survey. Ieee Access, 11, 9451-9468.
  • Stoll, C., Klaaßen, L., & Gallersdörfer, U. (2019). The carbon footprint of bitcoin. Joule, 3(7), 1647-1661.
  • Tratar, L. F., & Strmčnik, E. (2016). The comparison of Holt–Winters method and Multiple regression method: A case study. Energy, 109, 266-276.
  • Tschorsch, F., & Scheuermann, B. (2016). Bitcoin and beyond: A technical survey on decentralized digital currencies. IEEE Communications Surveys & Tutorials, 18(3), 2084-2123.
  • Winters, P. R. (1960). Forecasting sales by exponentially weighted moving averages. Management Science, 6(3), 324–342. https://doi.org/10.1287/mnsc.6.3.324
  • Yousaf, H., Kappos, G., & Meiklejohn, S. (2019). Tracing transactions across cryptocurrency ledgers. In 28th USENIX Security Symposium (USENIX Security 19 837-850.
  • Zhang, G., Patuwo, B. E., & Hu, M. Y. (1998). Forecasting with artificial neural networks: The state of the art. International journal of forecasting, 14(1), 35-62.
There are 71 citations in total.

Details

Primary Language English
Subjects Business Administration
Journal Section Research Article
Authors

Tahsin Galip Tekin 0000-0002-2642-5838

Sait Patır 0000-0002-1592-1094

Submission Date August 23, 2025
Acceptance Date February 17, 2026
Publication Date March 26, 2026
DOI https://doi.org/10.52791/aksarayiibd.1770977
IZ https://izlik.org/JA54CH99SK
Published in Issue Year 2026 Volume: 18 Issue: 1

Cite

APA Tekin, T. G., & Patır, S. (2026). Comparing Forecasting Powers Of Traditional Methods And Learning Based Methods In Cryptocurrency Market: An Application On Bitcoin, Ethereum, Binance Coin And Monero. Aksaray Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi, 18(1), 37-50. https://doi.org/10.52791/aksarayiibd.1770977