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

Jurnal Matematika, Statistika dan Komputasi · Vol. 17 No. 2 (2020) · pp. 166–177

DOI
10.20956/jmsk.v17i2.11803
ISSN
2614-8811, 1858-1382
Published
2020-12-23
Publisher
Department of Mathematics, Hasanuddin University
Language
en
License
creativecommons.org/licenses/by/4.0

Abstract

English

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.

Bahasa Indonesia

Dalam Ilmu statistika terdapat berbagai macam tipe data, salah satunya adalah data deret waktu. Data deret waktu merupakan serangkaian data pengamatan yang disusun berdasarkan urutan waktu. Data deret waktu dapat digunakan untuk meramalkan data yang akan datang sesuai dengan rentang waktu yang sama dengan sebelumnya dimana analisis tersebut adalah metode peramalan. Metode peramalan diterapkan pada pengambilan kebijakan dalam menentukan peredaran uang kartal di masyarakat yang dilakukan oleh Bank Indonesia (BI) adalah Inflow dan Outflow uang kartal. Inflow dan Outflow uang kartal merupakan data time series bulanan yang diduga dipengaruhi oleh waktu (time). Ada beberapa metode time series yang bisa digunakan untuk meramalkan Inflow dan Outflow uang kartal diantaranya: ARIMA, Time Series Regression (TSR), ARIMAX dan FFNN. Pemilihan metode terbaik menggunakan akurasi RMSE.

Keywords

Collaboration network of the authors

20 authors · 51 co-authorship links · 1 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Bandung Islamic University
  • IPB University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Hikmah HikmahAffiliation unknown2
Fetrika AnggrainiAffiliation unknown1
Bagus SartonoIPB University1
Khairil Anwar NotodiputroBandung Islamic University1
Nilawati NilawatiAffiliation unknown1
Elviani TandeAffiliation unknown1
Ignasius PresdaAffiliation unknown1
Aulia Atika MaghfirahAffiliation unknown1
Handayani HandayaniAffiliation unknown1
Ifa Khoiria NingrumAffiliation unknown1
Haris HarisAffiliation unknown1
Fardinah FardinahAffiliation unknown1
Rahmah AbubakarAffiliation unknown1
Nadia SalsabilahAffiliation unknown1
Jauhara Rana BudianiAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.unhas.ac.id/index.php/jmsk/oai, first harvested 5 Nov 2026, last seen 5 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">Forecasting Bank Indonesia Currency Inflow and Outflow Using ARIMA, Time Series Regression (TSR), ARIMAX, and NN Approaches in Lampung </dc:title>
	<dc:creator xml:lang="en">Qadrini, Laila</dc:creator>
	<dc:creator xml:lang="en">Asrirawan, Asrirawan</dc:creator>
	<dc:creator xml:lang="en">Mahmudah, Nur </dc:creator>
	<dc:creator xml:lang="en">Fahmuddin , Muhammad   </dc:creator>
	<dc:creator xml:lang="en">Amri, Ihsan Fathoni </dc:creator>
	<dc:subject xml:lang="en">Inflow</dc:subject>
	<dc:subject xml:lang="en">Outflow</dc:subject>
	<dc:subject xml:lang="en"> ARIMA</dc:subject>
	<dc:subject xml:lang="en">TSR</dc:subject>
	<dc:subject xml:lang="en"> ARIMAX</dc:subject>
	<dc:subject xml:lang="en">FFNN</dc:subject>
	<dc:description xml:lang="en">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.</dc:description>
	<dc:description xml:lang="id">Dalam Ilmu statistika terdapat berbagai macam tipe data, salah satunya adalah data deret waktu. Data deret waktu merupakan serangkaian data pengamatan yang disusun berdasarkan urutan waktu. Data deret waktu dapat digunakan untuk meramalkan data yang akan datang sesuai dengan rentang waktu yang sama dengan sebelumnya dimana analisis tersebut adalah metode peramalan. Metode peramalan diterapkan pada pengambilan kebijakan dalam menentukan peredaran uang kartal di masyarakat yang dilakukan oleh Bank Indonesia (BI) adalah Inflow dan Outflow uang kartal. Inflow dan Outflow uang kartal merupakan data time series bulanan yang diduga dipengaruhi oleh waktu (time). Ada beberapa metode time series yang bisa digunakan untuk meramalkan Inflow dan Outflow uang kartal diantaranya: ARIMA, Time Series Regression (TSR), ARIMAX dan FFNN. Pemilihan metode terbaik menggunakan akurasi RMSE.</dc:description>
	<dc:publisher xml:lang="en">Department of Mathematics, Hasanuddin University</dc:publisher>
	<dc:date>2020-12-23</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unhas.ac.id/index.php/jmsk/article/view/11803</dc:identifier>
	<dc:identifier>10.20956/jmsk.v17i2.11803</dc:identifier>
	<dc:source xml:lang="en">Jurnal Matematika, Statistika dan Komputasi; Vol. 17 No. 2 (2021): JANUARY 2021; 166-177</dc:source>
	<dc:source>2614-8811</dc:source>
	<dc:source>1858-1382</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.unhas.ac.id/index.php/jmsk/article/view/11803/6165</dc:relation>
	<dc:relation>10.20956/j.v17i2.11803.g6165</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2020 Author and publisher</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by/4.0</dc:rights>
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