基于支持向量机回归的民机航材消耗预测研究

航材作为装备保障的关键部件，其精确化保障在降低库存管理成本、优化资金分配、提高飞行安全等方面有重要作用。为保障飞机正常起飞，增加航空公司运营收入，降低航材保障费用，针对飞机航材消耗样本小、变化大难以预测的问题，提出一种基于支持向量机回归的航材消耗预测模型。以某国产民用飞机实际消耗数据为例，对支持向量机回归模型的预测精度进行验证。结果表明：该支持向量机回归模型对小样本数据有很好的适应性，相比指数平滑法具有更高的预测精度。

Prediction of Civil Aircraft Material Consumption Based on Support Vector Machine Regression

For the perpose of guarantee the normal take-off of aircraft, improve the operating income of airline companies and reduce the cost of aviation material guarantee, to address the problem that it is difficult to forecast aviation material consumption with small sample size and large variation, a time series-based support vector machine regression material consumption forecast model is proposed, and grid search is used to find the optimization of model parameters. Taking the actual consumption data of a domestic civil aircraft as an example, the forecast accuracy of the support vector machine regression method is verified, and the results prove that the method has good adaptability to small sample data and has higher forecast accuracy compared with the exponential smoothing method.