Open AccessPERAMALAN LAJU INFLASI DENGAN METODE AUTO REGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA)
Author(s) -
Djawoto Djawoto
Publication year - 2017
Publication title -
ekuitas
Language(s) - English
Resource type - Journals
eISSN - 2548-5024
pISSN - 1411-0393
DOI - 10.24034/j25485024.y2010.v14.i4.2190
Subject(s) - autoregressive integrated moving average , akaike information criterion , econometrics , inflation (cosmology) , moving average , statistics , economics , box–jenkins , time series , index (typography) , value (mathematics) , mathematics , computer science , physics , theoretical physics , world wide web
Auto Regression Integrated Moving Average (ARIMA) or the combination model of Auto Regression with moving average, is a linier model which is able to represent the stationary time series or non stationary time series. The purpose of this research is to forecast the inflation rate in November 2010 with the Consumer Price Index (CPI) by using ARIMA. The inflation indicator is very important to anticipate in making the Government’s policy and decision as well as for the citizen is for the information to determine what to do in related with savings and investment. By looking at the existing criteria, it is determined that the best model is ARIMA (1,1,0) or AR (1). Model ARIMA (1,1,0), the coefficient value AR (1) is significant,which has the most minimum value of Akaike Info Criterion (AIC) and Schwars Criterion (SC) compare toARIMA (0,1,1) or MA (1) and ARIMA (1,1,1) or AR (1) MA (1). In summarize, the ARIMA model used to forecast the valueof IHK is ARIMA (1,1,0).