Abstract
The interest in study using neural networks models has increased as they are able to capture nonlinear pattern and have a great accuracy. This paper focuses on how to determine the best model in feedforward neural networks for forecasting inflow and outflow in Indonesia. In univariate forecasting, inputs that used in the neural networks model were the lagged observations and it can be selected based on the significant lags in PACF. Thus, there are many combinations in order to get the best inputs for neural networks model. The forecasting result of inflow shows that it is possible to testing data has more accurate results than training data. This finding shows that neural networks were able to forecast testing data as well as training data by using the appropriate inputs and neuron, especially for short term forecasting. Moreover, the forecasting result of outflow shows that testing data were lower accurate than training data.
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References
Maier, H.R., Dandy, G.C.: Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environ. Model Softw. 15, 101–124 (2000)
Kandil, N., Wamkeue, R., Saad, M., Georges, S.: An efficient approach for short term load forecasting using artificial neural networks. Electr. Power Energy Syst. 28, 525–530 (2006)
Azad, H.B., Mekhilef, S., Ganapathy, V.G.: Long-term wind speed forecasting and general pattern recognition using neural networks. IEEE Trans. Sustain. Energy 5, 546–553 (2014)
Claveria, O., Torra, S.: Forecasting tourism demand to Catalonia: neural networks vs. time series models. Econ. Model. 36, 220–228 (2014)
Kara, Y., Boyacioglu, M.A., Baykan, O.K.: Predicting direction of stock price index movement using artificial neural networks and support vector machines: the sample of the Istanbul stock exchange. Expert Syst. Appl. 38, 5311–5319 (2014)
Guler, H., Talasli, A.: Modelling the daily currency in circulation in Turkey. Central Bank Repub. Turkey 10, 29–46 (2010)
Nasiru, S., Luguterah, A., Anzagra, L.: The efficacy of ARIMAX and SARIMA models in predicting monthly currency in circulation in Ghana. Math. Theory Model. 3, 73–81 (2013)
Rachmawati, N.I., Setiawan, S., Suhartono, S.: Peramalan Inflow dan dan Outflow Uang Kartal Bank Indonesia di Wilayah Jawa Tengah dengan Menggunakan Metode ARIMA, Time Series Regression, dan ARIMAX. Jurnal Sains dan Seni ITS, 2337–3520 (2015)
Kozinski, W., Swist, T.: Short-term currency in circulation forecasting for monetary policy purposes – the case of Poland. Financ. Internet Q. 11, 65–75 (2015)
Hill, T., Marquezb, L., O’Connor, M., Remusa, W.: Artificial neural network models for forecasting and decision making. Int. J. Forecast. 10, 5–15 (1994)
Crone, S.F., Kourentzes, N.: Input-variable specification for neural networks - an analysis of forecasting low and high time series frequency. In: International Joint Conference on Neural Networks, pp. 14–19 (2009)
Anders, U., Korn, O.: Model selection in neural networks. Neural Netw. 12, 309–323 (1999)
Wei, W.W.S.: Time Series Analysis Univariate and Multivariate Methods. Pearson, New York (2006)
Lee, M.H., Suhartono, S., Hamzah, N.A.: Calendar variation model based on ARIMAX for forecasting sales data with Ramadhan effect. In: Regional Conference on Statistical Sciences, pp. 349–361 (2010)
Sarle, W.S.: Neural networks and statistical models. In: Proceedings of the Nineteenth Annual SAS Users Group International Conference (USA 1994), SAS Institute, pp. 1538–1550 (1994)
Apriliadara, M., Suhartono, Prastyo, D.D.: VARI-X model for currency inflow and outflow forecasting with Eid Fitr effect in Indonesia. In: AIP Conference Proceedings, vol. 1746, p. 020041 (2016)
Proietti, T., Lutkepohl, H.: Does the Box-Cox transformation help in forecasting macroeconomic time series? Int. J. Forecast. 29, 88–99 (2013)
Faraway, J., Chatfield, C.: Time series forecasting with neural networks: a comparative study using the airline data. Appl. Stat. 47, 231–250 (1998)
Zhang, G.P., Qi, M.: Neural network forecasting for seasonal and trend time series. Eur. J. Oper. Res. 160, 501–514 (2005)
Suhartono, S., Subanar, S.: The effect of decomposition method as data preprocessing on neural networks model for forecasting trend and seasonal time series. Jurnal Keilmuan dan Aplikasi Teknik Industri 27–41 (2006)
Swanson, N.R., White, H.: Forecasting economic time series using flexible versus fixed specification and linear versus nonlinear econometric models. Int. J. Forecast. 13, 439–461 (1997)
Suhartono: New procedures for model selection in feedforward neural networks. Jurnal Ilmu Dasa, 9, 104–113 (2008)
Acknowledgments
This research was supported by DRPM-DIKTI under scheme of “Penelitian Berbasis Kompetensi”, project No. 532/PKS/ITS/2017. The authors thank to the General Director of DIKTI for funding and to anonymous referees for their useful suggestions.
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Suhartono, Saputri, P.D., Amalia, F.F., Prastyo, D.D., Ulama, B.S.S. (2017). Model Selection in Feedforward Neural Networks for Forecasting Inflow and Outflow in Indonesia. In: Mohamed, A., Berry, M., Yap, B. (eds) Soft Computing in Data Science. SCDS 2017. Communications in Computer and Information Science, vol 788. Springer, Singapore. https://doi.org/10.1007/978-981-10-7242-0_8
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DOI: https://doi.org/10.1007/978-981-10-7242-0_8
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