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Research and application of multi-variable grey optimization model with interactive effects in marine emerging industries prediction

Author

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  • Li, Xuemei
  • Wu, Xinran
  • Zhao, Yufeng

Abstract

Marine emerging industries have become an important growth point of the marine economy and a new driving force for the transformation and upgrading of the nation. Therefore, it is crucial to predict the future trend of output values of marine emerging industries for the development of the marine economy. To measure simultaneously the output value of China's three marine emerging industries (marine power industry, marine biopharmaceutical industry, and marine chemical industry) and considering the actual situation and internal connection of mutual influence and restriction among the three industries, the interaction term is introduced on the basis of the existing MGM(1,m) model, and a MGM(1,m) model with interactive effects (IMGM(1,m) model) is established. Also, the Particle Swarm Optimization (PSO) algorithm is used to optimize the predicted value in the model. The empirical results show that the MAPE of the training set is 2.04 %, 0.22 %, and 1.33 %, while those of the test set is 5.54 %, 3.68 %, and 4.80 %, respectively. The results of the proposed model are significantly better than other comparison models. Ultimately, the new model will be adopted to predict the output value of China's marine power, marine biopharmaceutical, and marine chemical industries from 2020 to 2023. It is estimated that by 2023, the output value of the three marine industries will reach 60.050, 59.790, and 84.497 billion yuan respectively, which will continue to maintain a growing trend. The forecast results will provide strong support for national policies and development plans for marine emerging industries.

Suggested Citation

  • Li, Xuemei & Wu, Xinran & Zhao, Yufeng, 2023. "Research and application of multi-variable grey optimization model with interactive effects in marine emerging industries prediction," Technological Forecasting and Social Change, Elsevier, vol. 187(C).
    • Handle: RePEc:eee:tefoso:v:187:y:2023:i:c:s0040162522007247
    DOI: 10.1016/j.techfore.2022.122203
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