Research of airlines shows that seat inventory control and therefore, revenue management is based not on a systematic analysis but more on human judgement. Machine learning models have been developed and applied to support decisions for ticket pricing dynamically. However, conventional models and approaches yield low statistical evaluation scores. In this study, the features used in other studies were explored and the cost available seat kilometer (CASK) value and target revenue features were included for the first time to the best of our knowledge which are essential components of ticket price decision. Real data from a low-cost carrier airline in Turkey were collected and the observation data were splitted into two to study with the highest profit sale data. Then the outliers were filtered to let the models learn from and generate better price offerings businesswise. Observation datasets obtained in each step were recorded to be tested. 7 different model techniques were simulated and tested with 4 different datasets according to 6 different statistical evaluation criteria. A new approach to Interpretable Multi-Variable Long Short-Term Memory (IMV-LSTM) model was proposed by taking every flight and its sales as an independent series, that is to assign a dynamic batch size. Extensive experiments on real datasets reveal enhanced statistical evaluation scores by using the proposed approach and model. The proposed model can be used by the airlines to mitigate human judgement on ticket pricing, to manage their price offerings to reach their target revenues and to increase their profits. The model can be used by other business cases that have similar historical data as overlapping windows structure.

航空公司的研究表明，座位库存控制以及收入管理不是基于系统分析，而是基于人为判断。已经开发了机器学习模型，并将其应用于动态支持票务定价的决策。但是，常规模型和方法的统计评估得分较低。在本研究中，我们探索了在其他研究中使用的功能，并据我们所知，这是门票价格决策的基本组成部分，首次包括了可用座位公里数（CASK）值和目标收入功能。收集了来自土耳其一家低成本航空公司的真实数据，并将观测数据分为两部分，以研究利润最高的销售数据。然后，筛选出离群值，以使模型能够从中学习并从商业角度生成更好的价格产品。记录在每个步骤中获得的观察数据集以进行测试。根据6种不同的统计评估标准，对7种不同的模型技术进行了仿真，并使用4个不同的数据集进行了测试。通过将每个航班及其销售量视为一个独立的系列，提出了一种可解释的多变量长期短期记忆（IMV-LSTM）模型的新方法，即分配了动态批次大小。通过使用所提出的方法和模型，对真实数据集的大量实验显示出增强的统计评估分数。航空公司可以使用所建议的模型来减轻人们对机票价格的判断，管理其价格产品以达到其目标收入并增加利润。