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All Journal Xplore: Journal of Statistics
Akbar Rizki
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Decision Sciences, Operations Research & Management Engineering Mathematics

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Analisis pada Data Harga Cabai Merah Keriting Indonesia menggunakan Model ARIMAX Muhammad Ali Umar; Farit Mochamad Afendi; Akbar Rizki; Budi Waryanto
Xplore: Journal of Statistics Vol. 7 No. 3 (2018): 31 Desember 2018
Publisher : Department of Statistics, IPB

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Abstract

The model used to analyze the time series data with one variable is Autoregresive Integrated Moving Average (ARIMA). In some cases, ARIMA model is not good enough in modeling. For instance, the time series data influenced by the outside patterns of observed variable that affect the variable. One way to capture the other patterns is with Autoregressive Integrated Moving Average Exogenous (ARIMAX). The model principle of ARIMAX is by making the other variables as the independent variables in the model used. Calender variation effects are independent variables which are often used in the modeling. In this research, ARIMAX model is applied on the weekly data of red curly chili in the period of Januari 1, 2011 to April 30, 2018. The evaluation result is there are some influential variables such as the peak of rainy season, election campaign, Eid Fitr, Eid al-Adha, and also Imlek. The best ARIMAX model gained is ARIMAX(1,1,2) model with the MAPE value of 5.054 â„….
Perbandingan Kinerja Regresi Conway-Maxwell-Poisson dan Poisson-Tweedie dalam Mengatasi Overdispersi Melalui Data Simulasi Ahmad Rifai Nasution; Kusman Sadik; Akbar Rizki
Xplore: Journal of Statistics Vol. 11 No. 3 (2022): Vol. 11 No. 3 (2022)
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (310.954 KB) | DOI: 10.29244/xplore.v11i3.1018

Abstract

Poisson regression is a standard method to model count data. Modeling count data frequently causes overdispersion which means that Poisson regression is less precise to model it as Poisson regression has the assumption of equidispersion. Overdispersion can be overcome by using Conway-Maxwell-Poisson (COM-Poisson) and Poisson Tweedie (Poisson-Tw) regression. The best model is determined based on the lowest value of RMSE, absolute bias, variance of parameter estimator, AIC, and BIC. This research uses simulation data. The response variable of simulation data is generated to follow Generalized Poisson distribution with combinations of and The result of simulation study shows that COM-Poisson and Compound Poisson-Tw are the alternatives to model overdispersed count data, but COM-Poisson is better to overcome overdispersion with higher dispersion parameter.
Co-Authors Ahmad Rifai Nasution Budi Waryanto Farit Mochamad Afendi Kusman Sadik Muhammad Ali Umar