On the evaluation of dynamic selection parameters for time series forecasting

Dynamic predictor selection has been applied to time series context to improve the accuracy to forecast. A crucial step in dynamic selection methods if the definition of the region of competence, which is composed of the most similar patterns to a test pattern, because the predictor that attains the best performance in this region is selected to forecast this test pattern. The performance of dynamic selection methods depends on two main parameters, the size of the region of competence and the similarity measure (also called of distance measure). This work evaluates the influence of these parameters on six real-world time series to forecasting one step. In the experiments, Bagging is adopted to generate a pool of predictors, where the best predictor is selected per query pattern based on its performance on the region of competence. The results show that the choice of an appropriate distance measure, as well as the size of the region of competence, is mandatory to boost the performance of the prediction system. Moreover, the results reinforce the importance of using a dynamic selection approach to improve forecasting accuracy when compared to the monolithic models, also called of single models.