Comparison of Exponentially Weighted Moving Average and Triple Exponential Smoothing Methods for Cryptocurrency Price Forecasting

Authors

  • Sukma Universitas Malikussaleh
  • Zarayunizar Zarayunizar Universitas Malikussaleh
  • Said Fadlan Anshari Universitas Malikussaleh

Keywords:

Cryptocurrency, EWMA, TES, Metric, Forecasting

Abstract

Cryptocurrencies have rapidly become a prominent part of today's information landscape. Bitcoin (BTC), one of the first cryptocurrencies, was introduced by Satoshi Nakamoto, a pseudonym whose true identity remains unknown. Nakamoto is credited with creating the blockchain system that underpins Bitcoin. As technology has advanced, cryptocurrencies have evolved into digital currencies that can be used as a medium of exchange. This has garnered significant attention from investors, particularly due to the substantial fluctuations in cryptocurrency values over time. Therefore, choosing the right method for making investment decisions is crucial. This research compares two leading methods for cryptocurrency price forecasting: Exponentially Weighted Moving Average (EWMA) and Triple Exponential Smoothing (TES). Each method has its own strengths and weaknesses in forecasting. In this study, EWMA achieved an average MAPE score of 54% and an MSE of 1818, while TES recorded an average MAPE of 45% and an MSE of 11408. The results indicate that TES outperforms EWMA by a margin of approximately 10%. To assess the methods' effectiveness, evaluation metrics were applied, categorizing performance as excellent, good, feasible, or not feasible.

References

[1] Wątorek, M., Drożdż, S., Kwapień, J., Minati, L., Oświęcimka, P., & Stanuszek, M. (2021). Multiscale characteristics of the emerging global cryptocurrency market. Physics Reports, 901, 1-82.

[2] Kevin George, “Cryptocurrency Regulations Around the World,” Investopedia. Accessed: Mar. 25, 2024. [Online]. Available: https://www.investopedia.com/cryptocurrency-regulations-around-the-world-5202122

[3] Artemov, N. M., Arzumanova, L. L., Sitnik, A. A., & Zenin, S. S. (2017). Regulation and Control of Virtual Currency: to be or not to be. Journal of Advanced Research in Law and Economics, 8(5 (27)), 1428-1435.

[4] Irma, Dasih, et al. "The future of cryptocurrency legality in Indonesia." Journal of Economics and Business Letters 1.1 (2021): 20-23.

[5] Hanley, Brian P. "The false premises and promises of Bitcoin." arXiv preprint arXiv:1312.2048 (2013).

[6] S. F. Asnhari, P. H. Gunawan and Y. Rusmawati, "Predicting Staple Food Materials Price Using Multivariables Factors (Regression and Fourier Models with ARIMA)," 2019 7th International Conference on Information and Communication Technology (ICoICT), Kuala Lumpur, Malaysia, 2019, pp. 1-5, doi: 10.1109/ICoICT.2019.8835193. keywords: {Predictive models;Linear regression;Mathematical model;Data models;Correlation;Computational modeling;Oils;Staple food;price;predicting;ARIMA;multiple linear regression;Fourier,

[7] Al-Omoush, Khaled Saleh, Ana M. Gomez-Olmedo, and Andrés Gómez Funes. "Why do people choose to continue using cryptocurrencies?." Technological Forecasting and Social Change 200 (2024): 123151.

[8] Zanardi, Maurizio, and Hizamrul Jaen. "APPLICATION OF TRIPLE EXPONENTIAL SMOOTHING METHOD FOR PREDICTING THE PRICE MOVEMENT OF CRYPTOCURRENCY ETHEREUM." Bulletin of Engineering Science, Technology and Industry 1.2 (2023): 66-75.

[9] Khedmati, M., F. Seifi, and M. J. Azizi. "Time series forecasting of bitcoin price based on autoregressive integrated moving average and machine learning approaches." International Journal of Engineering 33.7 (2020): 1293-1303.

[10] Irina, Cvetkova. "Cryptocurrencies legal regulation." BRICS law journal 5.2 (2018): 128-153.

[11] Gamal Thabroni. (2022). Forecasting: Definition, Factors, Methods, Steps, etc. Similar.Id. https://serupa.id/forecasting-peramalan-pengertian-faktor-metode-langkah-dsb/

[12] Kourentzes, N., Petropoulos, F., & Trapero, J. R. (2014). Improving forecasting by estimating time series structural components across multiple frequencies. International Journal of Forecasting, 30(2), 291-302.

[13] Boritz, J. Efrim, and Won Gyun No. "How significant are the differences in financial data provided by key data sources? A comparison of XBRL, Compustat, Yahoo! Finance, and Google Finance." Journal of Information Systems 34.3 (2020): 47-75.

[14] Korstanje, Joos. Advanced forecasting with Python. United States: Apress, 2021.

[15] Singh, Dalwinder, and Birmohan Singh. "Investigating the impact of data normalization on classification performance." Applied Soft Computing 97 (2020): 105524.

[16] Hunter, J. Stuart. "The exponentially weighted moving average." Journal of quality technology 18.4 (1986): 203-210.

[17] Dev, S., AlSkaif, T., Hossari, M., Godina, R., Louwen, A., & Van Sark, W. (2018, September). Solar irradiance forecasting using triple exponential smoothing. In 2018 International Conference on Smart Energy Systems and Technologies (SEST) (pp. 1-6). IEEE.

[18] González-Sopeña, J. M., V. Pakrashi, and B. Ghosh. "An overview of performance evaluation metrics for short-term statistical wind power forecasting." Renewable and Sustainable Energy Reviews 138 (2021): 110515.

[19] Lee, Dong Kyu, Junyong In, and Sangseok Lee. "Standard deviation and standard error of the mean." Korean journal of anesthesiology 68, no. 3 (2015): 220-223.

Downloads

Published

2024-12-27

Issue

Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)

Section

Articles

License

Copyright (c) 2024 Sukma, Zarayunizar Zarayunizar, Said Fadlan Anshari

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Copyright Notice

Authors published in this journal agree to the following terms:
1. The copyright of each article is retained by the author (s).
2. The author grants the journal the first publication rights with the work simultaneously licensed under the Creative Commons Attribution License, allowing others to share the work with an acknowledgment of authorship and the initial publication in this journal.
3. Authors may enter into separate additional contractual agreements for the non-exclusive distribution of published journal versions of the work (for example, posting them to institutional repositories or publishing them in a book), with acknowledgment of their initial publication in this journal.
4. Authors are permitted and encouraged to post their work online (For example in the Institutional Repository or on their website) before and during the submission process, as this can lead to productive exchanges, as well as earlier and larger citations of published work.
5. Articles and all related material published are distributed under a Creative Commons Attribution-ShareAlike 4.0 International License.