Civil airline fare prediction with a multi-attribute dual-stage attention mechanism

Abstract

Airfare price prediction is one of the core facilities of the decision support system in civil aviation, which includes departure time, days of purchase in advance and flight airline. The traditional airfare price prediction system is limited by the nonlinear interrelationship of multiple factors and fails to deal with the impact of different time steps, resulting in low prediction accuracy. To address these challenges, this paper proposes a novel civil airline fare prediction system with a Multi-Attribute Dual-stage Attention (MADA) mechanism integrating different types of data extracted from the same dimension. In this method, the Seq2Seq model is used to add attention mechanisms to both the encoder and the decoder. The encoder attention mechanism extracts multi-attribute data from time series, which are optimized and filtered by the temporal attention mechanism in the decoder to capture the complex time dependence of the ticket price sequence. Extensive experiments with actual civil aviation data sets were performed, and the results suggested that MADA outperforms airfare prediction models based on the Auto-Regressive Integrated Moving Average (ARIMA), random forest, or deep learning models in MSE, RMSE, and MAE indicators. And from the results of a large amount of experimental data, it is proven that the prediction results of the MADA model proposed in this paper on different routes are at least 2.3% better than the other compared models.