Open AccessThe Autoregressive Integrated Moving Average (ARIMA) Model for Predicting Jakarta Composite Index
Author(s) -
Didik Gunawan,
Weni Astika
Publication year - 2022
Publication title -
jurnal informatika ekonomi bisnis
Language(s) - English
Resource type - Journals
ISSN - 2714-8491
DOI - 10.37034/infeb.v4i1.114
Subject(s) - autoregressive integrated moving average , statistics , index (typography) , autoregressive model , composite index , econometrics , sample (material) , time series , value (mathematics) , moving average , population , mathematics , computer science , demography , composite indicator , chemistry , chromatography , sociology , world wide web
The purpose of this study is to test the ability of the Autoregressive Integrated Moving Average (ARIMA) model to predict the value of the Jakarta Composite Index (JKSE) which fluctuates greatly due to the Covid-19 pandemic. The population in this study is JKSE daily closing price data for the period January 2020 to April 2021, so the sample in this study is 324 time series data. The results showed that the best ARIMA model for predicting the value of the Jakarta Composite Index was ARIMA (3,1,9). ARIMA (3,1,9) can predict the JKSE value very well because the value of the forecasting results is not much different from the actual value. This is also evidenced by the results of the accuracy test using MAPE which has a result of 1,729 which means the accuracy of forecasting is 98,27%.