Electronic Money Transactions Forecasting with Support Vector Regression (SVR) and Vector Autoregressive Moving Average (VARMA)

Authors

  • I Nengah Dharma Pradnyandita Telkom University

DOI:

https://doi.org/10.21108/ijoict.v8i1.632

Keywords:

Electronic money, forecasting, multivariate time series, SVR, VARMA

Abstract

In today's digital era, the trend of payments with electronic money is rising. Some people have switched to do their way to the modern method such as electronic money.  This is to improve the efficiency of the financial system.  However, with the convenience and speed provided, if the use of electronic money is not being controlled properly, this can cause an unmanageable price of goods. In the context of controlling the risk of the use of electronic money, it is required to predict the use of electronic money in Indonesia. This paper, by using multivariate data analysis with the variable of electronic money transaction and Money supply (M1) as supporting variables in order to predict the nominal of electronic money transactions. The methods used are Vector Autoregressive Moving Average (VARMA) and Support Vector Regression (SVR). The results of the forecasting model will be compared using Mean Absolute Percentage Error (MAPE). According to the research that had been done, the SVR model had a better result compared to VARMA with a MAPE value of 3.577 %. This shows that the prediction data of the SVR model is close to actual data  

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References

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Published

2022-08-20

How to Cite

I Nengah Dharma Pradnyandita. (2022). Electronic Money Transactions Forecasting with Support Vector Regression (SVR) and Vector Autoregressive Moving Average (VARMA). International Journal on Information and Communication Technology (IJoICT), 8(1), 69–85. https://doi.org/10.21108/ijoict.v8i1.632

Issue

Vol. 8 No. 1 (2022): June 2022

Section

Data Science

License

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