Design of Experiment to Optimize the Architecture of Wavelet Neural Network for Forecasting the Tourist Arrivals in Indonesia

Otok, Bambang Widjanarko; Suhartono, Suhartono; Ulama, Brodjol Sutijo Suprih; Endharta, Alfonsus Julanto

doi:10.1007/978-3-642-25462-8_2

Cited by 3 publications

(1 citation statement)

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“…In addition, one of the main issues related to the accuracy of the DNN model is the determination of optimal inputs that follows the predictors and variable lags in the ARIMAX model. This is in line with some previous researches which shows that the main factor determining the prediction accuracy on the NN model is the corresponding input (Otok et al 2011;Suhartono et al 2018b). One of the main inputs in this study is dummy variable for calendar variation effect.…”

Section: Discussionsupporting

confidence: 91%

Deep Neural Network for Forecasting Inflow and Outflow in Indonesia

Suhartono¹,

Ashari²,

Prastyo³

et al. 2019

JSM

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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.

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Section: Discussionsupporting

confidence: 91%