We propose DOBRO, a light online learning module, which is equipped with a smart correction policy helping making decision to correct or not the given prediction depending on how likely the correction will lead to a better prediction performance. DOBRO is a standalone module requiring nothing more than a time series of prediction errors and it is flexible to be integrated into any black-box model to improve its performance under drifts. We performed evaluation in a real-world application with bus arrival time prediction problem. The obtained results show that DOBRO improved prediction performance significantly meanwhile it did not hurt the accuracy when drift does not happen.
['Alexandr V. Maslov', 'Hoang Thanh Lam', 'Mykola Pechenizkiy', 'Eric Bouillet', 'Tommi Kärkkäinen']
DOBRO: a prediction error correcting robot under drifts