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针对航班延误难以预测的问题，采用支持向量机回归方法建立航班到港延误预测模型。首先，采用相空间重构理论计算到港延误的延迟时间、嵌入维数和最大Lyapunov指数，发现到港延误时间序列存在混沌特性；将航班到港延误时间序列进行相空间重构，并结合执飞该航班的航空器在上游机场的离港延误构建模型的输入向量；其次，将粒子群算法、差分进化算法和遗传算法进行比较，用于选择最优的模型参数，实验表明，差分进化算法能够以较高概率获得最优的预测模型；最后，比较该模型、单一因素预测模型和相关向量机预测模型的航班延误预测性能。结果表明，该模型的预测性能明显优于另外两种模型，能够有效预测航班延误。

Flight Delay Prediction Using Support Vector Machine Regression

To solve the problem that the flight delay is difficult to predict, the support vector machine regression method is used to establish the flight arrival delay prediction model. First, the phase space reconstruction theory is used to calculate the flight arrival delay’s the delay time, embedded dimension and maximum Lyapunov exponent, and the chaotic characteristics of the flight arrival delay time sequence is found. The phase space of the flight arrival delay time sequence is reconstructed and combined with the departure delay of the upstream airport’s flight using the same aircraft to build the input variable vector of the prediction model. Second, for selecting the optimal model parameters, the particle swarm algorithm, differential evolution algorithm and genetic algorithm are compared, the experiment shows that differential evolution algorithm can get the optimal prediction model with a higher probability. Last, the prediction performance of the model, the single factor prediction model and the relevance vector machine prediction model are compared. The results show that the prediction performance of the model is much better than the other two, the model can effectively predict flight delays.