Modeling Data Containing Outliers using ARIMA Additive Outlier (ARIMA-AO)
Methodology
2018-03-02 v1
Abstract
The aim this study is discussed on the detection and correction of data containing the additive outlier (AO) on the model ARIMA (p, d, q). The process of detection and correction of data using an iterative procedure popularized by Box, Jenkins, and Reinsel (1994). By using this method we obtained an ARIMA models were fit to the data containing AO, this model is added to the original model of ARIMA coefficients obtained from the iteration process using regression methods. This shows that there is an improvement of forecasting error rate data.
Cite
@article{arxiv.1803.00257,
title = {Modeling Data Containing Outliers using ARIMA Additive Outlier (ARIMA-AO)},
author = {Ansari Saleh Ahmar and Suryo Guritno and Abdurakhman and Abdul Rahman and Awi and Alimuddin and Ilham Minggi and M. Arif Tiro and M. Kasim Aidid and Suwardi Annas and Dian Utami Sutiksno and S. Ahmar Dewi and H. Ahmar Kurniawan and A. Abqary Ahmar and Ahmad Zaki and Dahlan Abdullah and Robbi Rahim and Heri Nurdiyanto and Rahmat Hidayat and Darmawan Napitupulu and Janner Simarmata and Nuning Kurniasih and Leon Andretti Abdillah and Andri Pranolo and Haviluddin and Wahyudin Albra and A. Nurani M Arifin},
journal= {arXiv preprint arXiv:1803.00257},
year = {2018}
}
Comments
13 pages