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Neural Processing Letters

, Volume 50, Issue 2, pp 1789–1829 | Cite as

Several Novel Dynamic Ensemble Selection Algorithms for Time Series Prediction

  • Changsheng Yao
  • Qun DaiEmail author
  • Gang Song
Article
  • 125 Downloads

Abstract

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.

Keywords

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) 

Notes

Acknowledgements

This work is supported by the National Natural Science Foundation of China under the Grant No. 61473150.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjingChina

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