We propose that the ability to extract regularities from time series through prediction learning can be enhanced if we use a hierarchical architecture in which higher layers are trained to predict the internal state of lower layers when such states change significantly. This hierarchical organization has two functions: (a) it forces the system to recode sensory information progressively so as to enhance useful regularities and filter out useless information; and (b) it progressively reduces the length of the sequences which should be predicted going from lower to higher layers. This, in turn, allows higher levels to extract higher-level regularities which are hidden at the sensory level. By training an architecture of this type to predict the next sensory state of a robot navigating in an environment divided into two rooms, we show how the first-level prediction layer extracts low-level regularities such as 'walls', 'corners' and 'corridors', while the second-level prediction layer extracts higher-level regularities such as 'the left side wall of the large room'. The extraction of these regularities allows the robot to localize its position in the environment and to detect changes in the environment (e.g. the presence of a new object or the fact that a door has been closed).