Skip to main content

Can We Apply Traditional Forecasting Models to Predicting Bitcoin?

  • Conference paper
  • First Online:
City, Society, and Digital Transformation (INFORMS-CSS 2022)

Part of the book series: Lecture Notes in Operations Research ((LNOR))

Included in the following conference series:

  • 474 Accesses


As cryptocurrency becomes more accepted as a valid investment tool within financial markets, and as more financial instruments move on to a decentralized finance platform, demand for more advance methods of modeling cryptocurrency have increased. Having a reliable model will improve investor’s confidence in an otherwise high-risk and highly volatized market. Many researchers attempt to create new models and find new variables to forecasting cryptocurrency, however, developing a model that is consistent, accurate, and nondeterministic is still challenging. Nevertheless, traditional models have been proven over time when used for financial market analysis. In this paper, logistic regression and ARIMA will be the key statistical models investigated for use in forecasting Bitcoin. Each model will be tweaked to optimize performance based on current standing research. Furthermore, each model’s result will be scored and compared based on their ability to predict Bitcoin’s performance.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others



Augmented Dickey Fuller Test


Akaike’s Information Criteria score


Artificial Neural Networks


AutoRegressive Integrated Moving Average


Bayesian Information Criteria




K-Nearest Neighbor


  1. Hicks, C. (2020). The history of bitcoin | Investing | US News. US News & World Report. Accessed July 09, 2021.

  2. Tredinnick, L. (2019). Cryptocurrencies and the blockchain. Business Information Review, 36(1), 39–44.

    Article  Google Scholar 

  3. Arora, S. (2021). Understanding cryptocurrency and its benefits. Accessed June 27, 2021.

  4. Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system (p. 9).

    Google Scholar 

  5. Reiff, N. (2021). How much of all money is in bitcoin? Investopedia, May 31, 2021. Accessed June 27, 2021.

  6. 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.

    Article  Google Scholar 

  7. Ballings, M., Van den Poel, D., Hespeels, N., & Gryp, R. (2015). Evaluating multiple classifiers for stock price direction prediction. Expert Systems with Applications, 42(20), 7046–7056.

    Article  Google Scholar 

  8. Disci, S. (2020) Time series forecasting: KNN vs. ARIMA. R-bloggers, September 29, 2020. Accessed February 20, 2021.

  9. Jamili Zaini, B., Mansor, R., Yusof, N., & Hui Sang, B. (2019). Classify stock market movement based on technical analysis indicators using logistic regression. Journal of Advanced Research in Business and Management Studies, 1.

  10. Syed, S., Mubeen, M., Hussain, A., & Lal, I. (2018). Prediction of stock performance by using logistic regression model: Evidence from Pakistan Stock Exchange (PSX). Asian Journal Empirical Research, 8, July 2018.

  11. Jarrett, J., & Kyper, E. (2011). ARIMA modeling with intervention to forecast and analyze Chinese stock prices. International Journal of Engineering Business Management.

    Article  Google Scholar 

  12. Chen, S., Liu, Z., Wang, L., & Hu, J. (2020). Stability of a delayed competitive model with saturation effect and interval biological parameters. Journal of Applied Mathematics and Computing, 64(1), 1–15.

    Article  Google Scholar 

  13. Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018). Statistical and machine learning forecasting methods: Concerns and ways forward. PLoS ONE, 13(3), e0194889.

    Article  Google Scholar 

  14. Brownlee, J. (2018) Comparing classical and machine learning algorithms for time series forecasting. Machine Learning Mastery, October 30, 2018. Accessed July 09, 2021.

  15. Abraham, J., Higdon, D., Nelson, J., & Ibarra, J. (2018). Cryptocurrency price prediction using tweet volumes and sentiment analysis. 1(3), 22.

    Google Scholar 

  16. Yenidoğan, I., Çayir, A., Kozan, O., Dağ, T., & Arslan, Ç. (2018). Bitcoin forecasting using ARIMA and PROPHET. in 2018 3rd International conference on computer science and engineering (UBMK), September 2018, pp. 621–624.

  17. Chevallier, J., Guégan, D., & Goutte, S. (2021). Is it possible to forecast the price of bitcoin? Forecasting, 3(2), Art. no. 2, June 2021.

  18. Bambrough, B. (2020). As the bitcoin price soars, bitcoin’s ‘real’ crypto market dominance is revealed. Forbes. Accessed June 27, 2021.

  19. Chambers, C. (2020). ‘Bitcoin and stocks’ correlation reveal a secret. Forbes. Accessed July 23, 2021.

  20. Kim, J.-M., Kim, S.-T., & Kim, S. (2020). On the relationship of cryptocurrency price with US stock and gold price using copula models. Mathematics, 8(11), Art. no. 11, November 2020.

  21. Thaker, H.M.T., & Mand, A.A. (2021) Bitcoin and stock markets: A revisit of relationship. Journal of Derivatives and Quantitative Studies: 선물연구, 29(3), 234–256, January 2021.

  22. Akaike, H. (1998) Information theory and an extension of the maximum likelihood principle. In E. Parzen, K. Tanabe, & G. Kitagawa, (Eds.) ,Selected papers of Hirotugu Akaike (pp. 199–213). Springer.

  23. Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6(2), 461–464.

    Article  Google Scholar 

  24. Hiransha, M., Gopalakrishnan, E.A., Menon, V.K., & Soman, K.P. (2018). NSE stock market prediction using deep-learning models. Procedia Computer Science, 132, pp1351–1362, January 2018.

  25. Varghese, A., Tarhen, H., Shaikh, A., Banik, P., & Ramdasi, A. (2016) Stock market prediction using time series. International Journal on Recent and Innovation Trends in Computing and Communication, 4, 427–430, May 2016. ISSN 2321-8169

    Google Scholar 

  26. Spilak, B. (2018). Deep neural networks for cryptocurrencies price prediction, p. 73.

    Google Scholar 

  27. Shih, K.-H., Cheng, C.-C., & Wang, Y.-H. (2011). Financial information fraud risk warning for manufacturing industry—using logistic regression and neural network. Romanian Journal of Economic Forecasting , 18.

    Google Scholar 

  28. Troncoso Lora, A., Riquelme, J.C., Martínez Ramos, J.L., Riquelme Santos, J.M., & Gómez Expósito, A. (2003) Influence of kNN-based load forecasting errors on optimal energy production. In Progress in artificial intelligence, Berlin, Heidelberg, pp. 189–203.

  29. Vega, E., Flores, J., & Graff, M. (2014) k-nearest-neighbor by differential evolution for time series forecasting, November 2014, pp. 50–60.

  30. Sarfarz, A. (2017). Why smart contracts in blockchain need to avoid non-deterministic functions—DZone security. Accessed July 22, 2021.

  31. Mironiuc, M., & Robu, M.-A. (2013). Obtaining a practical model for estimating stock performance on an emerging market using logistic regression analysis. Procedia—Social and Behavioral Sciences, 81, 422–427.

    Article  Google Scholar 

  32. Ariyo, A.A., Adewumi, A.O., & Ayo, C.K. (2014) Stock price prediction using the ARIMA model. In 2014 UKSim-AMSS 16th international conference on computer modelling and simulation, Cambridge, United Kingdom, March 2014, pp. 106–112.

  33. Alahmari, S. (2019) Using machine learning ARIMA to predict the price of cryptocurrencies, July 2019.

    Google Scholar 

  34. Desev, K., Kabaivanov, S., & Desevn, D. (2019) Forecasting cryptocurrency markets through the use of time series models. The Business and Economic Horizons (BEH), 15(2). Accessed: July 22, 2021.

  35. Umadevi, B., Sundar, D., & Alli, P. (2013) An effective time series analysis for stock trend prediction using ARIMA model for nifty midcap-50. Undefined. Accessed: May 28, 2021. [Online]. Available: /paper/An-Effective-Time-Series-Analysis-for-Stock-Trend-Umadevi-Sundar/356879c2fc72465f5885315a16102975c6716226

    Google Scholar 

  36. Christy Jackson, J., Prassanna, J., Quadir, M.A., & Sivakumar, V. (2021). Stock market analysis and prediction using time series analysis. Material Today Proceedings, January 2021.

  37. Ayaz, Z., Fiaidhi, J., Sabah, A., & Ansari, M. (2020). Bitcoin price prediction using ARIMA model.

  38. Peng, J., Lee, K., & Ingersoll, G. (2002). An introduction to logistic regression analysis and reporting. The Journal of Educational Research, 96, 3–14.

    Article  Google Scholar 

  39. Kabria. Logistic regression for machine learning and classification. Kambria, July 09, 2019. Accessed October 12, 2021.

  40. M’ng, J.C.P., & Zainudin, R. (2016) Assessing the efficacy of adjustable moving averages using ASEAN-5 currencies. PLOS ONE, 11(8), e0160931, August 2016.

  41. Zielak. Bitcoin historical data, March 2021. Accessed October 17, 2020.

  42. Watkins, T. (2021). How the use of moving averages can create the appearance of confirmation of theories where none exists. Accessed February 24, 2021.

  43. Nau, R. (2020). Stationarity and differencing of time series data. Accessed November 7, 2020.

  44. Hyndman, R. J., & Khandakar, Y. (2008). Automatic time series forecasting: The forecast package for R. Journal of Statistical Software, 27, 1–22.

    Article  Google Scholar 

  45. Inan, S. (2018). Are cryptocurrency price changes predictable. Northeastern University.

    Article  Google Scholar 

Download references


I would like to acknowledge my advisor, Professor Szu-way Shu, whom helped guide me through the strategic aspects of this research. Additionally, I would like to acknowledge the contributions of Di He, Ph.D. and Songquan Pang for their support in this paper.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Wesley Szuway Shu .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Bobea, M., Shu, W.S. (2022). Can We Apply Traditional Forecasting Models to Predicting Bitcoin?. In: Qiu, R., Chan, W.K.V., Chen, W., Badr, Y., Zhang, C. (eds) City, Society, and Digital Transformation. INFORMS-CSS 2022. Lecture Notes in Operations Research. Springer, Cham.

Download citation

Publish with us

Policies and ethics