Adaptive Linear Regression for Data Stream

The approaches that currently constitute the stateof-the-art for the task of regression on continuous data streams usually involve ensembles, regression trees, and regression rules. They have been found to work very well for certain situations but generally consume computational resources to a prohibitive extent. In this paper, we propose a new method based on an ensemble of linear regressions for the regression task adapted to handle continuous data streams. The technique has been named Adaptive Linear Regression (ALR). The algorithm combines strategies that contribute to high prediction accuracy using (i) distinct sliding window sizes for training each ensemble element, and (ii) a dynamic regressor selection method for final ensemble voting. After an extensive experimental study, ALR was found to exhibit high predictive performance and outperform state-of-theart ensemble regressors on data streams for real and synthetic datasets. Moreover, it exhibits low processing time in its parallel version and is faster than ARF-Reg in its serial version. The paper also presents an analysis of how the choice of sliding window size for training favors accuracy.