Forecasting Bank Indonesia Currency Inflow and Outflow Using ARIMA, Time Series Regression (TSR), ARIMAX, and NN Approaches in Lampung

Authors

  • Laila Qadrini Program Studi Statistika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Sulawesi Barat
  • Asrirawan Asrirawan
  • Nur Mahmudah
  • Muhammad Fahmuddin
  • Ihsan Fathoni Amri

DOI:

https://doi.org/10.20956/jmsk.v17i2.11803

Keywords:

Inflow, Outflow, ARIMA, TSR, ARIMAX, FFNN

Abstract

There are various types of data, one of which is the time-series data. This data type is capable of predicting future data with a similar speed as the forecasting method of analysis.  This method is applied by Bank Indonesia (BI) in determining currency inflows and outflows in society. Moreover, Inflows and outflows of currency are monthly time-series data which are assumed to be influenced by time. In this study, several forecasting methods were used to predict this flow of currency including ARIMA, Time Series Regression (TSR), ARIMAX, and NN. Furthermore, RMSE accuracy was used in selecting the best method for predicting the currency flow. The results showed that the ARIMAX method was the best for forecasting because this method had the smallest RMSE.

References

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Published

2020-12-23

Issue

Vol. 17 No. 2 (2021): JANUARY 2021

Section

Research Articles

License

Copyright (c) 2020 Author and publisher

Creative Commons License

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

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

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