In the paper, the flight time deviation of Lithuania airports has been analyzed. The supervised machine learning model has been implemented to predict the interval of time delay deviation of new flights. The analysis has been made using seven algorithms: probabilistic neural network, multilayer perceptron, decision trees, random forest, tree ensemble, gradient boosted trees, and support vector machines. To find the best parameters which give the highest accuracy for each algorithm, the grid search has been used. To evaluate the quality of each algorithm, the five measures have been calculated: sensitivity/recall, precision, specificity, F-measure, and accuracy. All experimental investigation has been made using the newly collected dataset from Lithuania airports and weather information on departure/landing time. The departure flights and arrival flights have been investigated separately. To balance the dataset, the SMOTE technique is used. The research results showed that the highest accuracy is obtained using the tree model classifiers and the best algorithm of this type to predict is gradient boosted trees.

中文翻译：

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基于监督机器学习模型的立陶宛机场飞行时间偏差预测

本文分析了立陶宛机场的飞行时间偏差。已实施监督机器学习模型以预测新航班的时延偏差间隔。使用七个算法进行了分析：概率神经网络，多层感知器，决策树，随机森林，树集合，梯度提升树和支持向量机。为了找到为每种算法提供最高准确性的最佳参数，已使用了网格搜索。为了评估每种算法的质量，已计算出五种量度：灵敏度/召回率，精密度，特异性，F-测量和准确性。所有实验研究都是利用立陶宛机场新收集的数据集以及有关起飞/降落时间的天气信息进行的。出发航班和到达航班已分别进行了调查。为了平衡数据集，使用了SMOTE技术。研究结果表明，使用树模型分类器可获得最高的精度，而这种类型的最佳预测算法是梯度增强树。