The application of Elman recurrent neural network model for forecasting consumer price index of education, recreation and sports in Yogyakarta

Abstract

Recurrent neural network is a network which provides feedback connections. This network is believed to have a more powerful approach than the typical neural network for learning given data. The current research was aimed to apply the simplest recurrent neural network model, namely the Elman recurrent neural network (ERNN) model, to the consumer price index (CPI) of education, recreation, and sports data in Yogyakarta. The pattern of CPI data can be categorized as a function of time period, which tends to move upwards when the time period is increased, and jump at some points of the time period. This pattern was identified as similar to the pattern resulted by the function of the truncated polynomial spline regression model (TPSR). Hence, this research considered ERNN model which the inputs such as in the TPSR model were established by taking into account the location of the knot or jump points. In addition, the ERNN model with a single input, a time period was also generated. The results demonstrated that the two models have high accuracy both in training and testing data. More importantly, it was found that the first model is more appropriate than the second one in testing data.