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
Received:
1 September 2018/Accepted: 29 May 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
REFERENCES
Abdullah, M.I. & Nor-Muhammad, N.A. 2018. Prediction of colorectal
cancer driver genes from Patients' Genome Data. Sains
Malaysiana 47(12): 3095-3105.
Ahmad, I.S., Setiawan, S. & Masun, N.H. 2015. Forecasting of monthly inflow and outflow
currency using time series regression and ARIMAX: The Idul Fitri Effect. AIP Conference Proceedings 2015:
050002.
Alzahrani, A., Shamsi, P., Dagli,
C. & Ferdowsi, M. 2017. Solar irradiance forecasting using deep neural
network. Procedia Computer Science 114: 304-313.
Bai, Y., Chen, Z., Xie, J. & Li, C.
2016. Daily reservoir inflow forecasting using multiscale deep feature learning
with hybrid models. Journal of Hydrology 532: 193-206.
Bank Indonesia. 2011. Money Circulation. Gerai Info. pp. 1-8.
Bank Indonesia. 2018. Februari 17.
Metadata SSKI. http://www.bi.go.id/id/statistik/metadata/SSKI/
Documents/12Metadata%20Uang%20Kartal%20yang%20 Diedarkan.pdf.
Bowerman, B.L. & O'Connell, R.T. 1993. Forecasting and Time Series.
Belmont: Wadsworth Publishing Company.
Busseti, E. 2012. Deep Learning for Time Series Modelling. Stanford:
Stanford University.
Chen, Z. & Yang, Y.
2004. Assessing forecast accuracy measures. Preprint Series pp. 1-26.
Crone, S.F. & Kourentzes, N. 2009. Input-variable specification for
neural network - An analysis of forecasting low and high time series frequency. International Joint Conference on Neural Network doi:
10.1109/IJCNN.2009.5179046.
Golyandina, N. & Zhigljavsky, A. 2013. Singular Spectrum Analysis for
Time Series. Heidelberg: Springer-Verlag Berlin.
Goodfellow, I., Bengio, Y. & Courville, A.
2016. Deep Learning. Cambridge: MIT Press.
Gorr, W.L. 1994. Research
prospective on neural network forecasting. International Journal of
Forecasting 10(1): 1-4.
He, W. 2017. Load
forecasting via deep neural networks. Procedia Computer Science 122:
308-314.
Kumar, U. & Jain,
V.K. 2010. Time series models (Grey-Markov, Grey Model with rolling mechanism
and singular spectrum analysis) to forecast energy consumption in India. Energy 35: 1709-1716.
Lago, J., Ridder, F.D.
& Schutter, B.D. 2018. Forecasting spot
electricity prices: Deep learning approach and empirical comparison of
traditional algorithms. Applied Energy 221: 386-405.
Liu, H., Mi, X. & Li, Y. 2018. Smart multi-step deep learning
model for wind speed forecasting based on variational mode decomposition, singular spectrum analysis, LSTM network and ELM. Energy
Conversion and Management 159: 54-64.
Makridakis, S. & Hibon, M. 2000. The M3 competition: Results, conclusions,
and inmplications. International Journal of
Forecasting 16: 451-476.
Makridakis, S., Spiliotis, E. & Assimakopoulos,
V. 2018a. Statistical and machine learning forecasting methods: Concerns and
ways forward. PLoS ONE 13(3): e0194889.
Makridakis, S., Spiliotis, E. & Assimakopoulos,
V. 2018b. The M4 competition: Results, findings, conclusions and way forward. International
Journal of Forecasting. DOI: 10.1016/j.ijforecast.2018.06.001.
Otok, B.W., Suhartono, Ulama, B.S.S. & Endharta, A.J. 2011. Design of experiment to optimize the
architecture of wavelet neural network for forecasting the tourist arrivals in
Indonesia. Communications in Computer and Information Science 253(3):
14-23.
Setiawan, Suhartono,
Ahmad, I.S. & Rahmawati, N.I. 2015. Configuring
calendar variation based on time series regression method for forecasting of
monthly currency inflow and outflow in Central Java. AIP Conference
Proceedings 1691 2015: 050024.
Suhartono, Amalia,
F.F., Saputri, P.D., Rahayu,
S.P. & Ulama, B.S.S. 2018a. Simulation study for
determining the best architecture of multilayer perceptron for forecasting
nonlinear seasonal time series. Journal of Physics: Conference Series 1028(1):
012214.
Suhartono, Saputri,
P.D., Prastyo, D.D. & Rahayu,
S.P. 2018b. Hybrid quantile regression neural network model for forecasting
currency inflow and outflow in Indonesia. Journal of Physics: Conference
Series 1028(1): 012213.
Suhartono, Setyowati,
E., Salehah, N.A., Lee, M.H., Rahayu,
S.P. & Ulama, B.S.S. 2017. A hybrid singular
spectrum analysis and neural networks for forecasting inflow and outflow
currency of Bank Indonesia. In. Soft Computing in Data Science, edited
by Yap B., Mohamed A. & Berry, M. Singapore: Springer Nature. pp. 3-18.
Suhartono. 2007. Feedforward neural
network for time series forecasting. Yogyakarta: PhD Dissertation,
Gadjah Mada
University (Unpublished).
Vautard, R. & Ghil, M. 1989. Singular spectrum analysis in nonlinear
dynamics, with applications to paleoclimatic time
series. Physica D: Nonlinear Phenomena 35:
395-424.
Yu, C., Li, Y. &
Zhang, M. 2017. Comparative study on three new hybrid models using Elman Neural
Network and Empirical Mode Decomposition based technologies improved by
Singular Spectrum Analysis for hour-ahead wind speed forecasting. Energy
Conversion and Management 147: 76-85.
Zhang, X., Wang, J.
& Zhang, K. 2017. Short-term electric load forecasting based on singular
spectrum analysis and support vector machine optimized by Cuckoo search
algorithm. Electric Power Systems Research 146: 270-285.
Zubaidi, S.L., Dooley, J., Alkhaddar, R.M., Abdellatif, M.,
Al-Bugharbee, H., Ortega. & Martorell,
S.A. 2018. Novel approach for predicting monthly water demand by combining
singular spectrum analysis with neural networks. Journal of Hydrology 561:
136-145.
*Corresponding
author; email: mhl@utm.my
|