Augmenting adaptation with retrospective model correction for non-stationary regression problems

Existing adaptive predictive methods often use multiple adaptive mechanisms as part of their coping strategy in non-stationary environments. We address a scenario when selective deployment of these adaptive mechanisms is possible. In this case, deploying each adaptive mechanism results in different candidate models, and only one of these candidates is chosen to make predictions on the subsequent data. After observing the error of each of candidate, it is possible to revert the current model to the one which had the least error. We call this strategy retrospective model correction. In this work we aim to investigate the benefits of such approach. As a vehicle for the investigation we use an adaptive ensemble method for regression in batch learning mode which employs several adaptive mechanisms to react to changes in the data. Using real world data from the process industry we show empirically that the retrospective model correction is indeed beneficial for the predictive accuracy, especially for the weaker adaptive mechanisms.