Deep neural network for forecasting inflow and outflow in Indonesia

An optimal planning in the preparation of Money Requirement Plan (MRP) by Bank Indonesia is highly beneficial to maintain the availability of money in the community. One of the main factors needed in preparing of MRP is an accurate information about inflow and outflow. This study is to apply Deep Ne...

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Main Authors:	Suhartono, Ashari, Dimas Ewin, Prastyo, Dedy Dwi, Kuswanto, Heri, Muhammad Hisyam Lee
Format:	Article
Language:	English
Published:	Penerbit Universiti Kebangsaan Malaysia 2019
Online Access:	http://journalarticle.ukm.my/13903/ http://journalarticle.ukm.my/13903/ http://journalarticle.ukm.my/13903/1/26%20Suhartono.pdf

id	ukm-13903
recordtype	eprints
spelling	ukm-139032020-01-10T08:55:57Z http://journalarticle.ukm.my/13903/ Deep neural network for forecasting inflow and outflow in Indonesia Suhartono, Ashari, Dimas Ewin Prastyo, Dedy Dwi Kuswanto, Heri Muhammad Hisyam Lee, An optimal planning in the preparation of Money Requirement Plan (MRP) by Bank Indonesia is highly beneficial to maintain the availability of money in the community. One of the main factors needed in preparing of MRP is an accurate information about inflow and outflow. This study is to apply Deep Neural Network (DNN) for forecasting inflow and outflow in Indonesia and to compare its performance to ARIMAX as a simpler method and hybrid Singular Spectrum Analysis and DNN (SSA-DNN) as a more complex method. This study focuses on determining the best inputs in DNN, particularly for forecasting time series. A simulation study is used for evaluating the performance of each method related to the patterns in the time series. The real data are monthly inflow and outflow on 5 banknotes denominations from January 2003 to December 2016. The performance was evaluated based on Root Mean Square Error Prediction and Symmetry Mean Absolute Percentage Error Prediction criteria. The results of the simulation study showed that DNN yielded a more accurate forecast than ARIMAX and hybrid SSA-DNN in predicting time series with a trend, seasonal, calendar variation, and nonlinear noise patterns. Moreover, the results of inflow and outflow forecasting showed that DNN provided a more accurate prediction on most all banknotes denominations compared to ARIMAX and hybrid SSA-DNN. In general, these results show that DNN as machine learning model outperforms both ARIMAX as a simpler statistical model and hybrid SSA-DNN as a more complex model. Penerbit Universiti Kebangsaan Malaysia 2019-08 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/13903/1/26%20Suhartono.pdf Suhartono, and Ashari, Dimas Ewin and Prastyo, Dedy Dwi and Kuswanto, Heri and Muhammad Hisyam Lee, (2019) Deep neural network for forecasting inflow and outflow in Indonesia. Sains Malaysiana, 48 (8). pp. 1787-1798. ISSN 0126-6039 http://www.ukm.my/jsm/malay_journals/jilid48bil8_2019/KandunganJilid48Bil8_2019.html
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Kebangasaan Malaysia
building	UKM Institutional Repository
collection	Online Access
language	English
description	An optimal planning in the preparation of Money Requirement Plan (MRP) by Bank Indonesia is highly beneficial to maintain the availability of money in the community. One of the main factors needed in preparing of MRP is an accurate information about inflow and outflow. This study is to apply Deep Neural Network (DNN) for forecasting inflow and outflow in Indonesia and to compare its performance to ARIMAX as a simpler method and hybrid Singular Spectrum Analysis and DNN (SSA-DNN) as a more complex method. This study focuses on determining the best inputs in DNN, particularly for forecasting time series. A simulation study is used for evaluating the performance of each method related to the patterns in the time series. The real data are monthly inflow and outflow on 5 banknotes denominations from January 2003 to December 2016. The performance was evaluated based on Root Mean Square Error Prediction and Symmetry Mean Absolute Percentage Error Prediction criteria. The results of the simulation study showed that DNN yielded a more accurate forecast than ARIMAX and hybrid SSA-DNN in predicting time series with a trend, seasonal, calendar variation, and nonlinear noise patterns. Moreover, the results of inflow and outflow forecasting showed that DNN provided a more accurate prediction on most all banknotes denominations compared to ARIMAX and hybrid SSA-DNN. In general, these results show that DNN as machine learning model outperforms both ARIMAX as a simpler statistical model and hybrid SSA-DNN as a more complex model.
format	Article
author	Suhartono, Ashari, Dimas Ewin Prastyo, Dedy Dwi Kuswanto, Heri Muhammad Hisyam Lee,
spellingShingle	Suhartono, Ashari, Dimas Ewin Prastyo, Dedy Dwi Kuswanto, Heri Muhammad Hisyam Lee, Deep neural network for forecasting inflow and outflow in Indonesia
author_facet	Suhartono, Ashari, Dimas Ewin Prastyo, Dedy Dwi Kuswanto, Heri Muhammad Hisyam Lee,
author_sort	Suhartono,
title	Deep neural network for forecasting inflow and outflow in Indonesia
title_short	Deep neural network for forecasting inflow and outflow in Indonesia
title_full	Deep neural network for forecasting inflow and outflow in Indonesia
title_fullStr	Deep neural network for forecasting inflow and outflow in Indonesia
title_full_unstemmed	Deep neural network for forecasting inflow and outflow in Indonesia
title_sort	deep neural network for forecasting inflow and outflow in indonesia
publisher	Penerbit Universiti Kebangsaan Malaysia
publishDate	2019
url	http://journalarticle.ukm.my/13903/ http://journalarticle.ukm.my/13903/ http://journalarticle.ukm.my/13903/1/26%20Suhartono.pdf
first_indexed	2023-09-18T20:05:53Z
last_indexed	2023-09-18T20:05:53Z
_version_	1777407155344769024

Deep neural network for forecasting inflow and outflow in Indonesia

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