Inferensi
Vol 1, No 1 (2018): Inferensi

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

Dwi Rantini (Department of Statistics, Institut Teknologi Sepuluh Nopember, Indonesia.)
Made Ayu Dwi Octavanny (Department of Statistics, Institut Teknologi Sepuluh Nopember, Indonesia)
Rumaisa Kruba (Department of Statistics, Institut Teknologi Sepuluh Nopember, Indonesia)
Heri Kuswanto (Department of Statistics, Institut Teknologi Sepuluh Nopember, Indonesia)
Kartika Fithriasari (Department of Statistics, Institut Teknologi Sepuluh Nopember, Indonesia.)

Article Info

Publish Date
15 Sep 2018

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.

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Journal Info
Inferensi
Website

Abbrev

inferensi

Publisher

Institut Teknologi Sepuluh Nopember Surabaya

Subject

Computer Science & IT Decision Sciences, Operations Research & Management Engineering Mathematics Social Sciences

Description

The aim of Inferensi is to publish original articles concerning statistical theories and novel applications in diverse research fields related to statistics and data science. The objective of papers should be to contribute to the understanding of the statistical methodology and/or to develop and ...