Application of decomposition-ensemble learning paradigm with phase space reconstruction for day-ahead PM concentration forecasting.

PM concentration have received considerable attention from meteorologists, who are able to notify the public and take precautionary measures to prevent negative effects on health. Therefore, establishing an efficient early warning system plays a critical role in fostering public health in heavily polluted areas. In this study, ensemble empirical mode decomposition and least square support vector machine (EEMD-LSSVM) based on Phase space reconstruction (PSR) is proposed for day-ahead PM concentration prediction, according to the application of a decomposition-ensemble learning paradigm.