Comparison between hybrid quantile regression neural network and autoregressive integrated moving average with exogenous variable for forecasting of currency inflow and outflow in bank Indonesia

• Main Authors: Prastyo, Dedy Dwi (Author), Suhartono, Suhartono (Author), Puka, Agnes Ona Bliti (Author), Lee, Muhammad Hisyam (Author)
• Format: Article
• Language: English
• Published: Penerbit UTM Press, 2018-11.
• Subjects: QA Mathematics
• Online Access: Get fulltext

 Some problems arise in time series analysis are nonlinearity and heteroscedasticity. Methods that can be used to analyze such problems are neural network and quantile regression. There are a lot of studies and developments on both methods, but the study that focuses on the performances of combination of these two methods applied in real case are still limited. Therefore, this study performed a comparison between hybrid Quantile Regression Neural Network (QRNN) and Autoregressive Integrated Moving Average with Exogenous Variable (ARIMAX). Both methods were employed to model the currency inflow and outflow from Bank Indonesia in Nusa Tenggara Timur province. Based on the empirical result, the hybrid QRNN method provided better forecasting for currency outflow whereas the ARIMAX resulted in better forecasting for the inflow.