Title of article :
Time Series Forecasting by Using Box-Jenkins Models
Author/Authors :
Gorgess, Hazim M. University of Baghdad - College of Education for Pure Science(Ibn AL-Haitham) - Department of Mathematics, Iraq , Ibrahim, Raghad University of Baghdad - College of Education for Pure Science(Ibn AL-Haitham) - Department of Mathematics, Iraq
Abstract :
In this paper we introduce a brief review about Box-Jenkins models. The acronym ARIMA stands for “autoregressive integrated moving average”. It is a good method to forecast for stationary and non stationary time series. According to the data which obtained from Baghdad Water Authority, we are modelling two series, the first one about pure water consumption and the second about the number of participants. Then we determine an optimal model by depending on choosing minimum MSE as criterion.
Keywords :
Forecasting , Box , Jenkins , autoregressive integrated moving average (ARIMA) , Autoregressive (AR) , moving average (MA) , autocorrelation function (ACF) , partial autocorrelation function (PACF)
Journal title :
Ibn Alhaitham Journal For Pure and Applied Science
Journal title :
Ibn Alhaitham Journal For Pure and Applied Science
