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.

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References

Bank Indonesia. 2013. Sumber: www.bi.go.id:http://www.bi.go.id/id/tentang-bi/fungsi bi/status/Contents. Diakses 10 November 2017.

Draper, N. R dan Smith, H. 1992. Analisis Regresi Terapan, Jakarta : PT Gramedia Pustaka Utama.

Endharta, A. J., Hamzah, N. A., & Suhartono. 2009. Development Of Calender Variation Model Based On Time Series Regression And ARIMAX for Forecasting Time Series Data With Islamic Calender Effect. Proc. ICCS-X Cairo, Egypt, 18, 20-23.

Makridakis, S., S. Wheelwright, and V.E. McGree. 1999. Metode dan Aplikasi Peramalan. Jakarta : Bina Rupa Aksara.

Perdana, A.S. 2012. Perbandingan Metode Time Series Regression dan ARIMAX pada Pemodelan data Penjualan Pakaian di Boyolali. Surabaya: ITS.

Preifer, P.E. and Doutch, S.J.1980. Identification and Interpretation of First Order Space –Time ARMA Models.

Wulansari, E. R., dan Suhartono. 2014. Peramalan Netflow Uang Kartal dengan Metode ARIMAX dan Radial Basis Function Network (Studi Kasus Di Bank Indonesia). Jurnal Sains dan Seni Pomits Vol. 3, No.2, 73-78.

Wei, W. S. 2006. Time Series Analysis. New York : Addison Wesley Publishing Company. Inc Kassambara A. 2017.

Widayati C.S.W, 2009. Komparasi beberapa Metode Estimasi Kesalahan Pengukuran. Jurnal Penelitian dan Evaluasi Pendidikan. Vol 13, No. 2.

Yunita, Tasna 2019. Peramalan Jumlah Penggunaan Kuota Internet Menggunakan Metode Autoregressive Integrated Moving Average (ARIMA). JOMTA Journal of Mathematics: Theory and Applications. Vol. 1, No. 2.

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Published

2020-12-23

How to Cite

Qadrini, L., Asrirawan, A., Mahmudah, N. ., Fahmuddin , M. . . ., & Amri, I. F. . (2020). Forecasting Bank Indonesia Currency Inflow and Outflow Using ARIMA, Time Series Regression (TSR), ARIMAX, and NN Approaches in Lampung . Jurnal Matematika, Statistika Dan Komputasi, 17(2), 166-177. https://doi.org/10.20956/jmsk.v17i2.11803

Issue

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

Section

Research Articles

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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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