In many learning systems, we assume that the attributes are independent in some way; if not independent, then at least measuring different observations of the object we are trying to recognise. In this case, however, we know that the attributes are not independent; in fact there is usually a high correlation. For example, consider a position sensor on a robot. We know that due to physical constraints, the position at time t+1 is correlated with the position at time t.
In fact, we can take advantage of the fact by making our temporal learning algorithms use the correlations to reduce the data significantly.