计算机与现代化 ›› 2020, Vol. 0 ›› Issue (11): 65-69.

• 数据库与数据挖掘 • 上一篇    下一篇

一种基于ARIMA和LSTM的民航旅客订座组合预测模型

  

  1. (山东大学数学学院,山东济南250100)
  • 出版日期:2020-12-03 发布日期:2020-12-03
  • 作者简介:赵烜(1991—),男,山东济南人,助理实验师,硕士,研究方向:机器学习,数据挖掘,科学与工程计算,E-mail: zhaox@sdu.edu.cn。
  • 基金资助:
    国家自然科学基金资助项目(11671233); 山东省重点研发计划项目(2018GGX101036); 山东大学数学学院机器学习研究项目

A Combined Prediction Model of Civil Aviation Passenger Reservation Based on ARIMA and LSTM

  1. (School of Mathematics, Shandong University, Jinan 250100, China)
  • Online:2020-12-03 Published:2020-12-03

摘要: 针对民航旅客订座预测问题,提出一种组合预测模型。首先,根据订座特性,设计缺失值、噪声数据预处理方法,使得历史订座数据得到有效校正。然后,为了提高预测准确性,建立自回归求积移动平均模型(ARIMA)以及基于特征选取长短时记忆网络(LSTM)的组合预测模型,并利用某航空公司实际订座数据,将该组合模型应用于旅客订座预测场景中,实验结果表明,本文提出的组合模型的MAE值、RMSE值均小于2个单项模型,预测结果更准确。

关键词: 预测模型, 自回归求积移动平均模型, 长短时记忆网络, 组合模型

Abstract: In this paper, a combined prediction model is proposed for the prediction of passenger reservation in civil aviation. First of all, according to the characteristics of reservation, this paper designs an algorithm dealing with missing values and noise so as to correct the reservation data effectively. Then, for improving prediction accuracy, a combined prediction model which is composed of the ARIMA and LSTM algorithm is constructed. The actual passenger reservation data of an airline company is used for the prediction. The experiments show that the values of MAE and RMSE of the combined model are smaller than single model before combination. The prediction result is also more accurate and applicable.

Key words: prediction model, autoregressive integrated moving average model, long short term memory network, combined model