Autoregressive Integrated Moving Average Model to Predict Graduate Unemployment in Indonesia

Autoregressive Integrated Moving Average Model to Predict Graduate Unemployment in Indonesia

Nowadays it is getting harder for higher education graduates in finding a decent job. This study aims to predict the graduate unemployment in Indonesia by using autoregressive integrated moving average (ARIMA) model. A time series data of the graduate unemployment from 2005 to 2016 is analyzed. The...

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Bibliographic Details
Main Author: Mahmudah Umi
Format: Article
Language:English
Published: Sciendo 2017-02-01
Series:Practice and Theory in Systems of Education
Subjects:
forecasting
time series
graduate unemployment
higher education
Online Access:http://www.degruyter.com/view/j/ptse.2017.12.issue-1/ptse-2017-0005/ptse-2017-0005.xml?format=INT
id doaj-art-9a87a649f60b439d8c9a962dbe55404f
recordtype oai_dc
spelling doaj-art-9a87a649f60b439d8c9a962dbe55404f2018-08-24T14:49:15ZengSciendoPractice and Theory in Systems of Education1788-25912017-02-01121435010.1515/ptse-2017-0005ptse-2017-0005Autoregressive Integrated Moving Average Model to Predict Graduate Unemployment in IndonesiaMahmudah Umi0State Islamic University of Pekalongan, Pekalongan, IndonesiaNowadays it is getting harder for higher education graduates in finding a decent job. This study aims to predict the graduate unemployment in Indonesia by using autoregressive integrated moving average (ARIMA) model. A time series data of the graduate unemployment from 2005 to 2016 is analyzed. The results suggest that ARIMA (1,2,0) is the best model for forecasting analysis, where there is a tendency of increasing number for the next ten periods. Furthermore, the average of point forecast for the next 10 periods is about 1,266,179 while its minimum value is 1,012,861 the maximum values is 1,523,156. Overall, ARIMA (1,2,0) provides an adequate forecasting model so that there is no potential for improvement.http://www.degruyter.com/view/j/ptse.2017.12.issue-1/ptse-2017-0005/ptse-2017-0005.xml?format=INTforecastingtime seriesgraduate unemploymenthigher education
institution Open Data Bank
collection Open Access Journals
building Directory of Open Access Journals
language English
format Article
author Mahmudah Umi
spellingShingle Mahmudah Umi
Autoregressive Integrated Moving Average Model to Predict Graduate Unemployment in Indonesia
Practice and Theory in Systems of Education
forecasting
time series
graduate unemployment
higher education
author_facet Mahmudah Umi
author_sort Mahmudah Umi
title Autoregressive Integrated Moving Average Model to Predict Graduate Unemployment in Indonesia
title_short Autoregressive Integrated Moving Average Model to Predict Graduate Unemployment in Indonesia
title_full Autoregressive Integrated Moving Average Model to Predict Graduate Unemployment in Indonesia
title_fullStr Autoregressive Integrated Moving Average Model to Predict Graduate Unemployment in Indonesia
title_full_unstemmed Autoregressive Integrated Moving Average Model to Predict Graduate Unemployment in Indonesia
title_sort autoregressive integrated moving average model to predict graduate unemployment in indonesia
publisher Sciendo
series Practice and Theory in Systems of Education
issn 1788-2591
publishDate 2017-02-01
description Nowadays it is getting harder for higher education graduates in finding a decent job. This study aims to predict the graduate unemployment in Indonesia by using autoregressive integrated moving average (ARIMA) model. A time series data of the graduate unemployment from 2005 to 2016 is analyzed. The results suggest that ARIMA (1,2,0) is the best model for forecasting analysis, where there is a tendency of increasing number for the next ten periods. Furthermore, the average of point forecast for the next 10 periods is about 1,266,179 while its minimum value is 1,012,861 the maximum values is 1,523,156. Overall, ARIMA (1,2,0) provides an adequate forecasting model so that there is no potential for improvement.
topic forecasting
time series
graduate unemployment
higher education
url http://www.degruyter.com/view/j/ptse.2017.12.issue-1/ptse-2017-0005/ptse-2017-0005.xml?format=INT
_version_ 1612672910955118592

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