TY - JOUR

T1 - Forecasting the Number of Prisoners in Nganjuk with Integer-Valued Pth-Order Autoregressive (INAR(P))

AU - Belinda, Bella

AU - Novita, Mila

N1 - Funding Information:
This work is supported by Program Publikasi Terindeks Internasional (PUTI) Prosiding 2020 funded by Directorate of Research and Development Universitas Indonesia No. NKB-3610/UN2.RST/HKP.05.00/2020.
Publisher Copyright:
© Published under licence by IOP Publishing Ltd.

PY - 2021/4/19

Y1 - 2021/4/19

N2 - The most used time series model is the time series model which assumes the variables being tested are continuous in a discrete time period and produces continuous values. Whereas in many applications, such as number of monthly accidents, number of doctor visits per year a person makes, etc., needed a discrete time series model to handle discrete variables and produce discrete values as well. Time series model that handles count or non-negative integer data is the Integer-valued Autoregressive model with the pth-order or INAR(p). INAR(p) is the counterpart of AR(p) model for integer data. To get integer results, this model uses binomial thinning operator which implements probabilistic operations with discrete distribution that are suitable to model count data such as Poisson and Binomial, also use median forecasting method. Model parameters will be estimated using the Yule-Walker method. In this research, INAR(p) time series model will be applied to number of prisoners in Nganjuk from April 2013 until July 2016 to help tackle overcapacity problems in prisons that led to many negative impacts. Through model specification, the best model for forecast the case is INAR(2). In this data, based on the measure of AIC, BIC, and AICc, the INAR(2) model achieved better performance than its AR(2) counterpart.

AB - The most used time series model is the time series model which assumes the variables being tested are continuous in a discrete time period and produces continuous values. Whereas in many applications, such as number of monthly accidents, number of doctor visits per year a person makes, etc., needed a discrete time series model to handle discrete variables and produce discrete values as well. Time series model that handles count or non-negative integer data is the Integer-valued Autoregressive model with the pth-order or INAR(p). INAR(p) is the counterpart of AR(p) model for integer data. To get integer results, this model uses binomial thinning operator which implements probabilistic operations with discrete distribution that are suitable to model count data such as Poisson and Binomial, also use median forecasting method. Model parameters will be estimated using the Yule-Walker method. In this research, INAR(p) time series model will be applied to number of prisoners in Nganjuk from April 2013 until July 2016 to help tackle overcapacity problems in prisons that led to many negative impacts. Through model specification, the best model for forecast the case is INAR(2). In this data, based on the measure of AIC, BIC, and AICc, the INAR(2) model achieved better performance than its AR(2) counterpart.

UR - http://www.scopus.com/inward/record.url?scp=85104749424&partnerID=8YFLogxK

U2 - 10.1088/1742-6596/1863/1/012064

DO - 10.1088/1742-6596/1863/1/012064

M3 - Conference article

AN - SCOPUS:85104749424

SN - 1742-6588

VL - 1863

JO - Journal of Physics: Conference Series

JF - Journal of Physics: Conference Series

IS - 1

M1 - 012064

T2 - International Conference on Mathematics, Statistics and Data Science 2020, ICMSDS 2020

Y2 - 11 November 2020 through 12 November 2020

ER -