Most of inflow and outflow data in Indonesia are characterized by trend, seasonal, calendar variation, and heterogeneous variance. This study proposed hybrid ARIMAX Quantile Regression model for forecasting data that have trend, seasonal, calendar variation, and heterogeneous variance. There are two types of data that we used in this research, i.e. simulation and real data. The real data are monthly inflow and outflow of Bank Indonesia at East Java Province per currency for the period 2003 to December 2016. There are three types of ARIMAX Quantile Regression models with different predictors that be used for forecasting both data. The results show that hybrid ARIMAX Quantile Regression model can capture accurately all patterns in the data. Moreover, this hybrid model yield better forecast than individual ARIMAX model at 8 of 14 currencies of inflow and outflow data in East Java Province. Thus, based on forecast accuracy criteria, i.e. RMSE, MAE and MdAE, it could be concluded that hybrid ARIMAX Quantile Regression tend to give better forecast than other individual method.
|Journal||Journal of Physics: Conference Series|
|Publication status||Published - 14 Jun 2018|
|Event||2nd International Conference on Statistics, Mathematics, Teaching, and Research 2017, ICSMTR 2017 - Makassar, Indonesia|
Duration: 9 Oct 2017 → 10 Oct 2017