Time trends may affect the results of experiments that are conducted sequentially. A simple, yet powerful, way to model such an experiment is to represent the trend by an autoregressive integrated moving average time series model. I show how such models can be used to jointly estimate factorial and time-order effects and how they can be used as a diagnostic device to detect time trends in complex experiments.