Spare parts consumption prediction based on AO-ELM

In order to overcome the limitation of small data samples in the prediction of spare parts consumption, which leads to low prediction accuracy, a prediction method of spare parts consumption quantity based on Extreme Learning Machine (ELM) was proposed. First, ELM algorithm was used to construct the prediction model of spare parts consumption quantity. Second, Aquila optimizer (AO) was introduced to optimize the ELM model performance and reduce the training time of the model. Finally, the superiority of the proposed method is verified by comparative experiments.