Spare Aeroengine Demand Prediction Model Based on Deep Croston Method

Abstract

The spare aeroengine demand prediction is a crucial issue in aeroengine fleet management. For more accurate predictions, a spare aeroengine demand prediction model is proposed based on the deep Croston method. The Croston framework is adopted to deal with the intermittent demand characteristic. The demand interval prediction model and the demand amount prediction model were developed by the long short-term memory deep learning network. Accompanied with the proposed spare aeroengine demand prediction model, a comprehensive evaluation method of intermittent demand prediction is proposed. The actual spare aeroengine demand data of an airline company were adopted to validate the proposed spare aeroengine demand prediction model. The traditional Croston method was regarded as the benchmark comparison model. Under the Croston framework, the machine learning methods, including back propagation neural network, support vector machine, gradient boosting decision tree, extreme gradient boosting tree, and multilayer perceptron network, were also adopted as the comparison models. The prediction results showed that the proposed spare aeroengine demand prediction model based on the deep Croston method achieved evident advantages. Compared with the traditional Croston method, the machine learning methods provide a satisfactory way for intermittent demand prediction.