Multi-output regression for imbalanced data stream

Abstract

In this article, we describe an imbalanced regression method for making predictions over imbalanced data streams. We present MORSTS (Multiple Output Regression for Streaming Time Series), an online ensemble regressors devoted to non-stationary and imbalanced data streams. MORSTS relies on several multiple output regressor submodels, adopts a cost sensitive weighting technique for dealing with imbalanced datasets, and handles overfitting by means of the K-fold cross validation. For assessment purposes, experiments have been conducted on known real datasets and compared with known base regression techniques.