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Zarayunizar Zarayunizar
Universitas Malikussaleh
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Comparison of Exponentially Weighted Moving Average and Triple Exponential Smoothing Methods for Cryptocurrency Price Forecasting sukma rizki; Zarayunizar Zarayunizar; Said Fadlan Anshari
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

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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.
Co-Authors Said Fadlan Anshari Sukma Rizki