Enhancing data stream predictions with reliability estimators and explanation

• Correction of incremental predictions using reliability estimators.
• Adaptation of reliability estimators to incremental learning.
• Adaptation of explanation methodology to incremental learning.
• Visualization techniques of concept drift in dynamic data streams.
• Applications of electricity load prediction.

Incremental learning from data streams is increasingly attracting research focus due to many real streaming problems (such as learning from transactions, sensors or other sequential observations) that require processing and forecasting in the real time. In this paper we deal with two issues related to incremental learning – prediction accuracy and prediction explanation – and demonstrate their applicability on several streaming problems for predicting electricity load in the future. For improving prediction accuracy we propose and evaluate the use of two reliability estimators that allow us to estimate prediction error and correct predictions. For improving interpretability of the incremental model and its predictions we propose an adaptation of the existing prediction explanation methodology, which was originally developed for batch learning from stationary data. The explanation methodology is combined with a state-of-the-art concept drift detector and a visualization technique to enhance the explanation in dynamic streaming settings. The results show that the proposed approaches can improve prediction accuracy and allow transparent insight into the modeled concept.