Fitting an Arima Model to a Poisson Process

The Autoregressive Integrated Moving Average (ARIMA) is normally used to fit data that are collected over time space in a stochastic process. The univariate Box- Jenkins Arima model technique was used to fit an appropriate model to the data set from two independent stochastic processes observed from a Poisson experiment. The fitted model to the count data help us to understand on how to generate a series of counted events within a time space and also to study the similar pattern and behavior of the random process observed during the analysis.