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Prediction Model of Aviation Spare Parts Demand Based on PCA-IPSO-LSSVM
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Hao XU1, Cai-yan TIAN2, Rui-ke MAO1
Science Technology and Engineering | 2025, 25(9) : 3938 - 3944
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Science Technology and Engineering | 2025, 25(9): 3938-3944
• Papers·Aeronautics and Astronautics •
Prediction Model of Aviation Spare Parts Demand Based on PCA-IPSO-LSSVM
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Hao XU1, Cai-yan TIAN2, Rui-ke MAO1
Affiliations
  • 1 Aircraft Repair & Overhaul Plant, Civil Aviation Flight University of China, Guanghan 618307, China
  • 2 Guanghan Branch, Civil Aviation Flight University of China, Guanghan 618307, China
Published: 2025-03-28 doi: 10.12404/j.issn.1671-1815.2400770
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In order to solve the problem of poor forecasting effect due to the large number of influencing factors of aviation material consumption and small amount of sample data. A prediction model for aircraft spare parts demand based on principal component analysis (PCA), improved particle swarm optimization (IPSO), and least squares support vector machine (LSSVM) was proposed. Firstly, the principal component analysis method was used to screen the main influencing factors of aviation spare parts, and then the improved particle swarm optimization algorithm was used to optimize the least square support vector machine parameter combination, and finally the selection results and optimization parameter combination were used to complete the PCA-IPSO-LSSVM aviation spare parts demand prediction model training. The results show that compared with the other four prediction models, the PCA-IPSO-LSSVM model has the highest prediction accuracy, and the RMSE and MRE of the test set are 3.24 and 4.23%, respectively, indicating that the model has good prediction precision and fitting effect.

aviation material demand prediction  /  principal component analysis  /  improved particle swarm optimization  /  least square support vector machine
Hao XU, Cai-yan TIAN, Rui-ke MAO. Prediction Model of Aviation Spare Parts Demand Based on PCA-IPSO-LSSVM[J]. Science Technology and Engineering, 2025 , 25 (9) : 3938 -3944 . DOI: 10.12404/j.issn.1671-1815.2400770
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Year 2025 volume 25 Issue 9
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Article Info
doi: 10.12404/j.issn.1671-1815.2400770
  • Receive Date：2024-01-26
  • Online Date：2025-07-09
  • Published：2025-03-28
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History
  • Received：2024-01-26
  • Revised：2024-12-04
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Affiliations
    1 Aircraft Repair & Overhaul Plant, Civil Aviation Flight University of China, Guanghan 618307, China
    2 Guanghan Branch, Civil Aviation Flight University of China, Guanghan 618307, China
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