For a random variable yt, the unconditional mean is simply the expected value, E( yt ). In contrast, the conditional mean of yt is the expected value of yt given a conditioning set of variables, Ot. A conditional mean model specifies a functional form for E(yt -Ot). For a static conditional mean model, the conditioning set of variables is measured contemporaneously with the dependent variable yt. An example of a static conditional mean model is the ordinary linear regression model. In time series econometrics, there is often interest in the dynamic behavior of a variable over time. A dynamic conditional mean model specifies the expected value of yt as a function of historical information. This book develops the most important conditional time series models: ARIMA models and ARIMAX models across Box-Jenkins Methodology. Examples developed with MATLAB are presented