Modeling Data Containing Outliers using ARIMA Additive Outlier (ARIMA-AO)
Ansari Saleh Ahmar, Suryo Guritno, Abdurakhman, Abdul Rahman, Awi,, Alimuddin, Ilham Minggi, M. Arif Tiro, M. Kasim Aidid, Suwardi Annas, Dian, Utami Sutiksno, S. Ahmar Dewi, H. Ahmar Kurniawan, A. Abqary Ahmar, Ahmad, Zaki, Dahlan Abdullah, Robbi Rahim, Heri Nurdiyanto
TL;DR
This paper discusses detecting and correcting additive outliers in ARIMA models using an iterative process, improving forecast accuracy by adjusting model coefficients based on outlier detection.
Contribution
It introduces a method for identifying and correcting additive outliers in ARIMA models through an iterative detection and adjustment process.
Findings
Improved forecasting accuracy after outlier correction.
Effective detection of additive outliers in time series data.
Enhanced ARIMA model fitting with outlier adjustments.
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.
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