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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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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