Forecasting Bitcoin using Double Exponential Smoothing Method Based on Mean Absolute Percentage Error

Febri Liantoni - Universitas Sebelas Maret, Indonesia
Arif Agusti - Institut Teknologi Adhi Tama Surabaya, Indonesia

Citation Format:



Abstract— After being introduced in 2008, the rise in the price of bitcoin and the popularity of other cryptocurrencies triggered a growing discussion about how much energy was consumed during the production of this currency. Making cryptocurrency the most expensive and most popular, both the business world and the research community have begun to study the devel-opment of bitcoin. In this study bitcoin price predictions are performed using the double exponential smoothing method based on the mean absolute percentage error (MAPE). The MAPE value is used to find the best alpha (α) parameter as the basis for bitcoin price forecasting. The dataset used is the price of bitcoin from 2017 to 2019. The dataset was obtained from As for the value of the alpha parameter (α), using a value of 0.1 to 0.9. Based on the test results using the double exponential smoothing method obtained the smallest MAPE value of 2.89%, with the best alpha (α) at 0.9. The prediction is done to see the price of bitcoin on January 1, 2020. The error rate generated on the predicted price of bitcoin uses an amount of 0.0373%. This shows that the system built can be used as a support for decision making when trading bitcoin.


bitcoin, cryptocurrency, double exponential smoothing, mean absolute percentage error

Full Text:



A. T. H. Yulia Safitri, Aziz Fathoni, “Analysis The Influence Of Risk Management And Investment Strategy On Value Added Investors With Online Trading As Intervening Variable,†J. Manage., vol. 4, no. 4, 2018.

S. Nakamoto, “Bitcoin: A Peer-to-Peer Electronic Cash System,†Jepang: Tokyo., 2010.

S. Baharaeen and A. S. Masud, “A computer program for time series forecasting using single and double exponential smoothing techniques,†Comput. Ind. Eng., vol. 11, no. 1–4, pp. 151–155, Jan. 1986.

A. Pranata, M. Akbar Hsb, T. Akhdansyah, and S. Anwar, “Penerapan Metode Pemulusan Eksponensial Ganda dan Tripel Untuk Meramalkan Kunjungan Wisatawan Mancanegara ke Indonesia,†J. Data Anal., vol. 1, no. 1, pp. 32–41, Sep. 2018.

A. L. Rosas and V. M. Guerrero, “Restricted forecasts using exponential smoothing techniques,†Int. J. Forecast., vol. 10, no. 4, pp. 515–527, Dec. 1994.

A. N. Aimran and A. Afthanorhan, “A comparison between single exponential smoothing (SES), double exponential smoothing (DES), holts (brown) and adaptive response rate exponential smoothing (ARRES) techniques in forecasting Malaysia population,†Glob. J. Math. Anal., vol. 2, no. 4, p. 276, Sep. 2014.

X. Li, “Comparison and Analysis Between Holt Exponential Smoothing and Brown Exponential Smoothing Used for Freight Turnover Forecasts,†in 2013 Third International Conference on Intelligent System Design and Engineering Applications, 2013, pp. 453–456.

D. A. Pratama, A. L. Dzulfida, J. K. Huwaida, A. Prabowo, and A. Tripena, “Aplikasi Metode Double Exponential Smoothing Brown Dan Holt Untuk Meramalkan Total Pendapatan Bea Dan Cukai,†in Prosiding Seminar Nasional Matematika dan Terapannya, 2016.

N. Salwa, N. Tatsara, R. Amalia, and A. F. Zohra, “Peramalan Harga Bitcoin Menggunakan Metode ARIMA (Autoregressive Integrated Moving Average),†J. Data Anal., vol. 1, no. 1, pp. 21–31, Sep. 2018.

H. Jaen, E. Darnila, and M. Fikry, “Aplikasi Peramalan Kurs Bitcoin-Rupiah Dengan Menggunakan Metode Double Exponential Smoothing,†TECHSI - J. Tek. Inform., vol. 11, no. 1, p. 106, May 2019.

T. Andriyanto, “Sistem Peramalan Harga Emas Antam Menggunakan Double Exponential Smoothing,†INTENSIF, vol. 1, no. 1, p. 1, Feb. 2017.

S. Küfeoğlu and M. Özkuran, “Bitcoin mining: A global review of energy and power demand,†Energy Res. Soc. Sci., vol. 58, p. 101273, Dec. 2019.

Y. Wu, A. Luo, and D. Xu, “Identifying suspicious addresses in Bitcoin thefts,†Digit. Investig., vol. 31, p. 200895, Dec. 2019.

N. D. Bhaskar and D. L. K. Chuen, “Bitcoin Mining Technology,†in

Handbook of Digital Currency, Elsevier, 2015, pp. 45–65.

N. Gandal, J. Hamrick, T. Moore, and T. Oberman, “Price manipulation in the Bitcoin ecosystem,†J. Monet. Econ., vol. 95, pp. 86–96, May 2018.

H. Liu et al., “Forecast of the trend in incidence of acute hemorrhagic conjunctivitis in China from 2011–2019 using the Seasonal Autoregressive Integrated Moving Average (SARIMA) and Exponential Smoothing (ETS) models,†J. Infect. Public Health, Jan. 2020.

A. de Myttenaere, B. Golden, B. Le Grand, and F. Rossi, “Mean Absolute Percentage Error for regression models,†Neurocomputing, vol. 192, pp. 38–48, Jun. 2016.


  • There are currently no refbacks.

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

JOIV : International Journal on Informatics Visualization
ISSN 2549-9610  (print) | 2549-9904 (online)
Organized by Society of Visual Informatocs, and Institute of Visual Informatics - UKM and Soft Computing and Data Mining Centre - UTHM
W :
E :,,

View JOIV Stats

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