In mining temporal data streams, Dynamic Ensemble Selection (DES) has emerged as one of the most promising approaches of ensemble methods based on the assumption that each member of the ensemble is an expert in some local area of the stream. The aim is to select, on the fly, according to a given test instance x, a subset of experts from a pool of various models. To this end, meta-learning has been widely studied to predict the performance of each base-model and accordingly select the best ones and combine their outputs to compute the final prediction. However, most of the existing selection methods for time series forecasting on data streams do not handle model's dependencies, and therefore maybe missing useful insights. In this paper, we propose a novel approach to harness the potential dependencies within base-models' behavior based on Incremental Multi-Target Regression (MTR) to achieve Dynamic Ensemble Selection (DES). We show that explicitly considering models' dependencies improves overall performance. This work is the first to use Incremental MTR for learning the behavior of each component in an ensemble of forecasters on data streams. Finally, we conduct an extensive experimental study to compare the performance of the proposed methods against state-of-the-art approaches.