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Modeling Data Containing Outliers using ARIMA Additive Outlier (ARIMA-AO)

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36. Ahmar_2018_J._Phys.__Conf._Ser._954_012010.pdf (551.3Kb)
Date
2018-02-22
Author
Ahmar, Ansari Saleh
Guritno, Suryo
Abdurakhman, Abdurakhman
Rahman, Abdul
Awi, Awi
Alimuddin, Alimuddin
Minggi, Ilham
Tiro, M Arif
Aidid, M Kasim
Annas, Suwardi
Sutiksno, Dian Utami
Ahmar, Dewi S
Ahmar, Kurniawan H
Ahmar, A Abqary
Zaki, Ahmad
Abdullah, Dahlan
Rahim, Robbi
Nurdiyanto, Heri
Hidayat, Rahmat
Napitupulu, Darmawan
Simarmata, Janner
Kurniasih, Nuning
Abdillah, Leon Andretti
Pranolo, Andri
Haviluddin, Haviluddin
Albra, Wahyudin
Arifin, A Nurani M
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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. In the simulation data is obtained that the data contained AO initial models are ARIMA (2,0,0) with MSE = 36,780, after the detection and correction of data obtained by the iteration of the model ARIMA (2,0,0) with the coefficients obtained from the regression Zt= 0,106 + 0, 204Zt-1 + 0, 401Zt-2 - 329X1(t) + 115X2(t) + 35,9X3(t) and MSE = 19,365. This shows that there is an improvement of forecasting error rate data.
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http://repository.unmul.ac.id/handle/123456789/3599
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Repository Universitas Mulawarman copyright ©   LP3M Universitas Mulawarman
Jalan Kuaro Kotak Pos 1068
Telp. (0541) 741118
Fax. (0541) 747479 - 732870
Samarinda 75119, Kalimantan Timur, Indonesia
Contact Us | Send Feedback