There is then a clear need to incorporate the autocorrelation effect into the Control Charts. Autoprojective tools or models are surrogates for omitted variables. In most cases, as long as true cause variables don't change, history is prologue. However, one should always try to collect and use information or data for the cause variables or potential cause variables. Box and Jenkins themselves took shortcuts and often were misled by the results.
Researchers point out that simple minded differencing rather than detrending can bear poor results.
Autobox deals with this issue. The failure has not been in the method, but in those implementing the method. I have been told that it is never a good idea to show students what shouldn't be done as they might get the idea that they should follow the example. To see an example of what you should studiously avoid please click on the arrow. The data should be allowed to mix'n match the combination of history,causals and dummies.