FORECASTING ELECTRONIC MONEY SPENDING FOR NON-BANK
Keywords:
Electronic Money, Winter’s Exponential Smoothing, Box-Jenkins Method, Combined ForecastingAbstract
This article aimed to develop a forecasting model for electronic money spending in non-bank. The monthly data was collected from the Bank of Thailand website from November 2015 to October 2021. The data were separated into 2 groups for training models (November 2015 – October 2020) and testing accuracy models (November 2020 – October 2021). The forecasting methods consist of Winter’s exponential smoothing method, the Box-Jenkins method and two combined forecasting methods consisting of the equal-weighted method and weight based on the ordinary least square coefficients method. The mean absolute percentage error (MAPE) and root mean square error (RMSE) were used to compare the accuracy of the models. The results show that the combined forecasting method using weight based on ordinary least square regression coefficients is the most appropriate model for forecasting electronic money for non-bank. This research is helpful to entrepreneurs and financial institutions in strategic plans and financial products designed to support the increase of electronic financial users.
Downloads
References
Financial Consumer Protection Center, Bank of Thailand. (2021). Electronic Money. Retrieved October 2021, from https://www.1213.or.th/th/serviceunderbot/payment/Pages/e-money.aspx
Bank of Thailand. (2010). e-money. Retrieved October 2021, from https://www.bot.or.th/App/BTWS_STAT/
statistics/BOTWEBSTAT.aspx?reportID=694&language=TH
Tippanyawong, K. (2016). A study of problems and stimuli to use mobile e-money in Thailand [Unpublished master’s thesis]. Thammasat University.
Kaewtan, J. (2014). Factor Influencing the Acceptance of Electronic Payment Using Smartphone Devices: In Case of Bangkok and Pathum Thani [Unpublished master’s thesis]. Rajamangala University of Technology Thanyaburi.
Widayat, W., Masudin, I., and Satiti, N. R. (2020). E-Money payment: Customers' adopting factors and the implication for open innovation. Journal of Open Innovation: Technology, Market, and Complexity, 6(3), Article 57.
Chavaratthanarungsee, P. (2016). Forecasting the value of using electronic money transection (e-money) by using time series data mining technique. Retrieved October 2021, from http://e-research.siam.edu
Chearnkaitpradab, J., and Akarapin, T. (2021). Applying the technical time series model for forecasting payment transaction volume through on mobile banking application. Management Sciences Valaya Alongkorn Review, 2(2), 36-54. (in Thai).
Bank of Thailand. (2021). Electronic Money Service Transaction Information (e-Money). Retrieved October 2021, from https://data.go.th/dataset/item_48172184-b2c6-4dad-b9f4-7442be87e1a2
Manmin, M. (2006). Time Series and Forecasting. Foreprinting, Bangkok. (in Thai).
Box, G. E. P., Jenkins, G. M., and Reinsel, G. C. (1994). Time Series Analysis: Forecasting and Control (3rd Edition). New Jersey. Prentice Hall. p. 332.
Dickey, D. A., and Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American statistical association, 74(366), 427-431.
Montgomery, D. C., Peck, E. A., and Vining, G. G. (2006). Introduction to Linear Regression Analysis (4th edition). New York. Wiley.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Journal of Srinakharinwirot University (Journal of Science and Technology)
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Srinakharinwirot University Journal of Sciences and Technology is licensed Under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International (CC-BY-NC-ND 4.0) License, Unless Otherwise Stated. Please Read Journal Policies Page for More Information on Open Access, Copyright and Permissions.
