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Research on combined model based on multi-objective optimization and application in time series forecast

  • Shenghui Zhang
  • Jiyang WangEmail author
  • Zhenhai Guo
Methodologies and Application
  • 35 Downloads

Abstract

Combined model theory has been widely used to forecast the time series problems that are often nonlinear, nonstationary and irregular. Current forecasting models based on combined models theory could adapt to some time series data and overcome some disadvantages of the single models. However, in previous studies, most forecasting models have just focused on improving the accuracy or stability. Nevertheless, for an effective forecasting model, considering only one criterion or rule (stability or accuracy) is insufficient. In this paper, a novel forecasting system, called non-dominated sorting genetic algorithm III combined system with three objective functions, was proposed and successfully employed to solve the predicament of electricity load forecasting which demands to obtain both high accuracy and strong stability as an example. Both stability and accuracy of our proposed combined system are superior to the model compared which showed in the experiment results.

Keywords

Combined model Multi-objective optimization Time series forecast Non-dominated sorting genetic algorithm III 

Notes

Acknowledgements

This work is supported by National Natural Science Foundation of China, Grant No. 41475013.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Mathematics and StatisticsLanzhou UniversityLanzhouPeople’s Republic of China
  2. 2.Faculty of Information TechnologyMacau University of Science and TechnologyMacauPeople’s Republic of China
  3. 3.State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingPeople’s Republic of China

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