• Supanee Wuttirawat Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok.
  • Apitchaya Wongluang Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok.
  • Chayanich Ponbanjong Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok.
  • Siraprapa Manomat Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok.


Electronic Money, Winter’s Exponential Smoothing, Box-Jenkins Method, Combined Forecasting


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.


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How to Cite

Wuttirawat, S., Wongluang, A., Ponbanjong, C., & Manomat, S. (2023). FORECASTING ELECTRONIC MONEY SPENDING FOR NON-BANK. Srinakharinwirot University Journal of Sciences and Technology, 15(30, July-December), 1–13, Article 251601. Retrieved from