Sains Malaysiana 48(8)(2019): 1787–1798

http://dx.doi.org/10.17576/jsm-2019-4808-26

Deep Neural Network for Forecasting Inflow and Outflow in Indonesia

(Rangkaian Saraf Dalam untuk Ramalan Aliran Masuk dan Aliran Keluar di Indonesia)

SUHARTONO¹, DIMAS EWIN ASHARI¹, DEDY DWI PRASTYO¹, HERI KUSWANTO¹ & MUHAMMAD HISYAM LEE²*

¹Institut Teknologi Sepuluh Nopember, Surabaya-60111, Indonesia

²Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor Darul Takzim, Malaysia

Diserahkan: 1 September 2018/Diterima: 29 Mei 2019

ABSTRACT

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.

Keywords: ARIMAX; DNN; inflow and outflow; SSA-DNN; time series forecasting

ABSTRAK

Suatu perancangan yang optimum dalam penyediaan Pelan Wang Keperluan (MRP) oleh Bank Indonesia sangat berfaedah untuk mengekalkan kewujudan wang dalam masyarakat. Salah satu faktor utama yang diperlukan dalam menyediakan MRP adalah maklumat yang tepat tentang aliran masuk dan aliran keluar. Kajian ini bertujuan untuk menerapkan Rangkaian Neural Dalam (DNN) untuk ramalan aliran masuk dan aliran keluar di Indonesia dan untuk membandingkan prestasi ARIMAX sebagai kaedah yang mudah dan hibrid Analisis Spektrum Singular dan DNN (SSA-DNN) sebagai satu kaedah yang lebih kompleks. Kajian ini tertumpu kepada menentukan input terbaik DNN, terutamanya bagi peramalan siri masa. Kajian simulasi yang digunakan untuk menilai prestasi setiap kaedah yang berkaitan dengan corak dalam siri masa. Data sebenar adalah aliran masuk dan aliran keluar bulanan pada 5 denominasi wang kertas dari Januari 2003 untuk Disember 2016. Prestasi dinilai berdasarkan ramalan punca min ralat kuasa dua dan ramalan simetri min mutlak peratusan ralat. Keputusan bagi kajian simulasi menunjukkan bahawa DNN menghasilkan ramalan yang lebih tepat berbanding ARIMAX dan hibrid SSA-DNN untuk meramalkan siri masa dengan kecenderungan, bermusim, perubahan kalendar dan corak bunyi tak linear. Selain itu, keputusan ramalan aliran masuk dan aliran keluar menunjukkan bahawa DNN membuat ramalan yang lebih tepat bagi kebanyakan denominasi wang kertas berbanding ARIMAX dan hibrid SSA-DNN. Secara amnya, keputusan ini menunjukkan bahawa DNN sebagai model pembelajaran mesin yang lebih baik berbanding ARIMAX sebagai model statistik mudah dan hibrid SSA-DNN sebagai model yang lebih kompleks.

Kata kunci: Aliran masuk dan aliran keluar; ARIMAX; DNN; SSA-DNN; waktu ramalan siri

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*Pengarang untuk surat-menyurat; email: mhl@utm.my