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所属单位：计算机科学与技术学院/人工智能学院/软件学院

发表刊物：NEURAL PROCESSING LETTERS

关键字：Dynamic ensemble selection (DES) DES algorithm based on Predictor Accuracy over Local Region (DES-PALR) Dynamic Ensemble Selection algorithm based on the Consensus of Predictors (DES-CP) Dynamic validation set determination algorithm Time series prediction (TSP)

摘要：The goal to improve prediction accuracy and robustness of predictive models is quite important for time series prediction (TSP). Multi-model predictions ensemble exhibits favorable capability to enhance forecasting precision. Nevertheless, a static ensemble system does not always function well for all the circumstances. This work proposes six novel dynamic ensemble selection (DES) algorithms for TSP, including one DES algorithm based on Predictor Accuracy over Local Region (DES-PALR), two DES algorithms based on the Consensus of Predictors (DES-CP) and three Dynamic Validation Set determination algorithms. The first dynamic validation set determination algorithm is designed based on the similarity between the Predictive value of the test sample and the Objective values of the training samples. The second one is constructed based on the similarity between the Newly constituted sample for the test sample and All the training samples. Finally, the third one is developed based on the similarity between the Output profile of the test sample and the Output profile of each training sample. These proposed algorithms successfully realize dynamic ensemble selection for TSP. Experimental results on twelve benchmark time series datasets have demonstrated that the proposed DES algorithms greatly improve predictive performance when compared against current state-of-the-art prediction algorithms and the static ensemble selection techniques.

ISSN号：1370-4621

是否译文：否

发表时间：2019-10-01

合写作者：姚长生,宋刚

通讯作者：戴群