@inproceedings{494ba44ef36d4140b9205a4efbcca0e0,
title = "A time series model: First-order integer-valued autoregressive (INAR(1))",
abstract = "Nonnegative integer-valued time series arises in many applications. A time series model: first-order Integer-valued AutoRegressive (INAR(1)) is constructed by binomial thinning operator to model nonnegative integer-valued time series. INAR (1) depends on one period from the process before. The parameter of the model can be estimated by Conditional Least Squares (CLS). Specification of INAR(1) is following the specification of (AR(1)). Forecasting in INAR(1) uses median or Bayesian forecasting methodology. Median forecasting methodology obtains integer s, which is cumulative density function (CDF) until s, is more than or equal to 0.5. Bayesian forecasting methodology forecasts h-step-ahead of generating the parameter of the model and parameter of innovation term using Adaptive Rejection Metropolis Sampling within Gibbs sampling (ARMS), then finding the least integer s, where CDF until s is more than or equal to u. u is a value taken from the Uniform(0,1) distribution. INAR(1) is applied on pneumonia case in Penjaringan, Jakarta Utara, January 2008 until April 2016 monthly.",
author = "Simarmata, {D. M.} and Fevi Novkaniza and Yekti Widyaningsih",
note = "Publisher Copyright: {\textcopyright} 2017 Author(s).; 2nd International Symposium on Current Progress in Mathematics and Sciences 2016, ISCPMS 2016 ; Conference date: 01-11-2016 Through 02-11-2016",
year = "2017",
month = jul,
day = "10",
doi = "10.1063/1.4991261",
language = "English",
series = "AIP Conference Proceedings",
publisher = "American Institute of Physics Inc.",
editor = "Sugeng, {Kiki Ariyanti} and Djoko Triyono and Terry Mart",
booktitle = "International Symposium on Current Progress in Mathematics and Sciences 2016, ISCPMS 2016",
}