BibTex Citation Data :
@article{Medstat9198, author = {Suparti Suparti and Alfi Sa'adah}, title = {ANALISIS DATA INFLASI INDONESIA MENGGUNAKAN MODEL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) DENGAN PENAMBAHAN OUTLIER}, journal = {MEDIA STATISTIKA}, volume = {8}, number = {1}, year = {2015}, keywords = {}, abstract = { The inflation data is one of the financial time series data which often has high volatility. It is caused by the presence of outlier s in the data. Therefore, it is necessary to analyze forecasting that can make all the assumptions are fulled without having to ignore the presence of outlier s. The aim of this study is analyzing the inflation data in Indonesia using ARIMA model with the outlier detection. By modeling annual inflation data in December 2006 to December 2013 there are two types of outlier that are additive outlier (AO) and level shift (LS) outlier . The results show that The ARIMA model with the addition of outlier are better than the ARIMA model without outlier . The ARIMA ([1.12], 1.0) model with the addition of 19 outlier s meet to the all assumptions that are the significance parameters, normality, homoscedasticity, and independence of residuals as well as the smallest MSE value. Keywords: Inflation, ARIMA, Outlier , MSE }, issn = {2477-0647}, pages = {1--11} doi = {10.14710/medstat.8.1.1-11}, url = {https://ejournal.undip.ac.id/index.php/media_statistika/article/view/9198} }
Refworks Citation Data :
The inflation data is one of the financial time series data which often has high volatility. It is caused by the presence of outliers in the data. Therefore, it is necessary to analyze forecasting that can make all the assumptions are fulled without having to ignore the presence of outliers. The aim of this study is analyzing the inflation data in Indonesia using ARIMA model with the outlier detection. By modeling annual inflation data in December 2006 to December 2013 there are two types of outlier that are additive outlier (AO) and level shift (LS) outlier. The results show that The ARIMA model with the addition of outlier are better than the ARIMA model without outlier. The ARIMA ([1.12], 1.0) model with the addition of 19 outliers meet to the all assumptions that are the significance parameters, normality, homoscedasticity, and independence of residuals as well as the smallest MSE value.
Keywords: Inflation, ARIMA, Outlier, MSE
Article Metrics:
Last update:
Forecasting time series data containing outliers with the ARIMA additive outlier method
Last update: 2024-11-20 08:05:52
The Authors submitting a manuscript do so on the understanding that if accepted for publication, copyright of the article shall be assigned to Media Statistika journal and Department of Statistics, Universitas Diponegoro as the publisher of the journal. Copyright encompasses the rights to reproduce and deliver the article in all form and media, including reprints, photographs, microfilms, and any other similar reproductions, as well as translations.
Media Statistika journal and Department of Statistics, Universitas Diponegoro and the Editors make every effort to ensure that no wrong or misleading data, opinions or statements be published in the journal. In any way, the contents of the articles and advertisements published in Media Statistika journal are the sole and exclusive responsibility of their respective authors and advertisers.
The Copyright Transfer Form can be downloaded here: [Copyright Transfer Form Media Statistika]. The copyright form should be signed originally and send to the Editorial Office in the form of original mail, scanned document or fax :
Dr. Di Asih I Maruddani (Editor-in-Chief) Editorial Office of Media StatistikaDepartment of Statistics, Universitas DiponegoroJl. Prof. Soedarto, Kampus Undip Tembalang, Semarang, Central Java, Indonesia 50275Telp./Fax: +62-24-7474754Email: maruddani@live.undip.ac.id
Media Statistika
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
Gedung F Lantai 3, Jalan Prof Jacub Rais, Kampus Tembalang
Semarang 50275
Indexing:
