Deep neural network for forecasting inflow and outflow in Indonesia

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.