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

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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.

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Acknowledgements

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

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Authors and Affiliations

  1. School of Mathematics and Statistics, Lanzhou University, Lanzhou, People’s Republic of China

    Shenghui Zhang

  2. Faculty of Information Technology, Macau University of Science and Technology, Macau, People’s Republic of China

    Jiyang Wang

  3. State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, People’s Republic of China

    Zhenhai Guo

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  1. Shenghui Zhang
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Correspondence to Jiyang Wang.

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Zhang, S., Wang, J. & Guo, Z. Research on combined model based on multi-objective optimization and application in time series forecast. Soft Comput 23, 11493–11521 (2019). https://doi.org/10.1007/s00500-018-03690-w

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