Abstract: To scientifically and accurately forecast the demand for aviation spare parts in airline inventories and develop rational material plans,this paper proposes a machine learning-based method.The approach considers factors such as spare part prices,importance,maintenance interval times,and installation quantities.Firstly,principal component analysis(PCA) and K-means clustering are applied to reduce dimensionality,enabling the visualization and classification of spare parts with different demand patterns.Then,it establishes hybrid kernel extreme learning machine(HKELM) and random forest(RF) models for multivariate regression predictions on the categorized data.To address the challenge of parameter selection in the prediction process,the sparrow search algorithm(SSA) is employed to iteratively optimize the optimal parameters of the two models.Finally,the method is validated with a case study using real operational data from a specific airline fleet.Its performance is compared with backpropagation(BP) neural networks,support vector machines(SVM),and least squares support vector machines(LSSVM).The results indicate that the proposed forecasting method achieves favorable outcomes and provides valuable guidance for airline material planning efforts.

Key words: Aviation material demand forecast, Dimensionality reduction clustering, Hybrid kernel extreme learning machine, Random forest, Sparrow search algorithm