Hybrid model for forecasting space-time data with calendar variation effects

Abstract

The aim of this research is to propose a new hybrid model, i.e. Generalized Space-Time Autoregressive with Exogenous Variable and Neural Network (GSTARX-NN) model for forecasting space-time data with calendar variation effect. GSTARX model represented as a linear component with exogenous variable particularly an effect of calendar variation, such as Eid Fitr. Whereas, NN was a model for handling a nonlinear component. There were two studies conducted in this research, i.e. simulation studies and applications on monthly inflow and outflow currency data in Bank Indonesia at East Java region. The simulation study showed that the hybrid GSTARX-NN model could capture well the data patterns, i.e. trend, seasonal, calendar variation, and both linear and nonlinear noise series. Moreover, based on RMSE at testing dataset, the results of application study on inflow and outflow data showed that the hybrid GSTARX-NN models tend to give more accurate forecast than VARX and GSTARX models. These results in line with the third M3 forecasting competition conclusion that stated hybrid or combining models, in average, yielded better forecast than individual models.