Forecasting currency circulation data of Bank Indonesia by using hybrid ARIMAX-ANN model

The purpose of this study is to forecast currency inflow and outflow data of Bank Indonesia. Currency circulation in Indonesia is highly influenced by the presence of Eid al-Fitr. One way to forecast the data with Eid al-Fitr effect is using autoregressive integrated moving average with exogenous in...

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Bibliographic Details
Main Authors: Prayoga, I. Gede Surya Adi, Suhartono, Rahayu, Santi Puteri
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
Correlation
Forecasting
Inflow
Mathematical models
Model accuracy
Muslim holidays
Outflow
Ramadan
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Summary:The purpose of this study is to forecast currency inflow and outflow data of Bank Indonesia. Currency circulation in Indonesia is highly influenced by the presence of Eid al-Fitr. One way to forecast the data with Eid al-Fitr effect is using autoregressive integrated moving average with exogenous input (ARIMAX) model. However, ARIMAX is a linear model, which cannot handle nonlinear correlation structures of the data. In the field of forecasting, inaccurate predictions can be considered caused by the existence of nonlinear components that are uncaptured by the model. In this paper, we propose a hybrid model of ARIMAX and artificial neural networks (ANN) that can handle both linear and nonlinear correlation. This method was applied for 46 series of currency inflow and 46 series of currency outflow. The results showed that based on out-of-sample root mean squared error (RMSE), the hybrid models are up to10.26 and 10.65 percent better than ARIMAX for inflow and outflow series, respectively. It means that ANN performs well in modeling nonlinear correlation of the data and can increase the accuracy of linear model.
ISSN:0094-243X
1551-7616
DOI:10.1063/1.4982867