A Guide to Box-Jenkins ModelinG

A Guide to Box-Jenkins ModelinG By George C. s. Wang Describes in simple language how to use Box-Jenkins models for forecasting … the key requirement of Box-Jenkins modeling is that time series is either stationary or can be transformed into one … the most difficult part in this type of modeling is the identification of a model. G G eorge Box and Gwilym Jenkins developed a statistical approach for time series modeling. Time series models developed on the basis of their approach are called Box-Jenkins models, also known as ARIMA models. A time series can be defined as a sequence of data observed over time. ARIMA models are univariate, that is, they are based on a single time series variable. Box and Jenkins have also developed procedures for multivariate modeling. However, in practice, even their univariate approach, sometimes, is not as well understood as the classic regression method. The objective of this article is to describe the basics of univariate Box- Jenkins models in simple and layman terms. uniVAriAte ModelinG The purpose of univariate modeling is to establish a relationship between the present value of a time series and its past values so that forecasts can be made ...