Quantile Regression Neural Network Model For Forecasting Consumer Price Index In Indonesia

Abstract

The main purpose of time series analysis is to obtain the forecasting result from an observation for future values. Quantile Regression Neural Network is a statistical method that can model data with non-homogeneous variance with artificial neural network approach that can capture nonlinear patterns in the data. Real data that allegedly have such characteristics is Consumer Price Index (CPI). CPI forecasting is important to assess price changes associated with cost of living as well as identifying periods of inflation or deflation. The purpose of this research is to compare several method of forecasting CPI in Indonesia. The data used in this study during January 2007 until April 2018 period. QRNN method will be compared with Neural Network with RMSE evaluation criteria. The result is QRNN is the best method for forecasting CPI with RMSE 0.95.

Comparison Of Support Vector Regression And Autoregressive Integrated Moving Average With Exogenous Variable On Indonesia Consumer Price Index

“…The results show that the CPI has a negative influence on interest rates. CPI research was also conducted by [18], using the Neural Network and Quantile Regression Neural Network methods on CPI data to produce a good RMSE value.…”