Fitting an Arima Model to a Poisson ProcessJournal: International Journal of Scientific Engineering and Research (IJSER) (Vol.3, No. 1)
Publication Date: 2015-01-05
Authors : J. B. Ehimony;
Page : 12-15
Keywords : ARIMA; Stationary; Non-Stationary; Difference; 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.
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