Forecasting Consumer Price Index of Education, Recreation, and Sport, using Feedforward Neural Network Model
نویسندگان
چکیده
The aim of this research is to forecast the consumer price index (CPI) of education, recreation, and sport in Indonesia using feedforward neural network (FFNN) model. We consider two FFNN models which are differed from the inputs. The inputs of the first model are generated by considering the inputs such as in a time series model, those are the lags of the CPI. Regarding that the pattern of the CPI data follow the segmented linear function, we generate the second model with the inputs such as in truncated polynomial spline regression model, by taking into account the location of the knots. The results demonstrate that the first model has better performance both in training and testing data. In addition, the first model is adequate model, means that the model delivers no autocorrelation error. Otherwise, the other model is not adequate model.
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