Forecasting of airfare prices using time series

Gordiievych, Anastasiia; Shubin, Igor

doi:10.1109/itib.2015.7355055

Cited by 4 publications

(2 citation statements)

References 4 publications

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“…Compared with the ReLU [11], a general activation function, the Leaky ReLU is used in this paper because it Fig. 5 Important factors affecting fares can reasonably divide the negative values.…”

Section: The Deep Learning Model For Fare Predictionmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: The Deep Learning Model For Fare Predictionmentioning

confidence: 99%

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

et al. 2021

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“…The method of data processing is used to mark the point process, and the process is predicted by using random forest and decision tree classifier [2]. A Lantseva, and K Mukhina establish a regression model to predict the price [3]. In 2015, W Groves and M Gini use feature selection techniques to give the lowest price for all flight forecasts [4].…”

Section: Introductionmentioning

confidence: 99%

Ticket price forecasting based on machine learning

2017

Proceedings of 2017 the 7th International Workshop on Computer Science and Engineering

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With the development of the aviation industry and the improvement of people's living standard, more and more people choose the aircraft as their way to travel, but the airline adjusts the price according to the revenue management in real time. Due to the large fluctuations in ticket prices, the price forecast has practical application value. This paper proposes a combination algorithm, combining the time series algorithm and random forests algorithm to model the ticket price data, and realizes the accurate prediction of the ticket price. The experimental results show that the combination algorithm model is more reliable by comparing the forecasting results with the actual results of each price model. The model is helpful for passengers to buy tickets and to save money.

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A Novel Approach to Predict Flight Fares in India Using SOM-LSSVM Model Approach

Raha,

Thangam

et al. 2024

2024 International Conference on Electronics, Computing, Communication and Control Technology (ICECCC)

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Forecasting of airfare prices using time series

Cited by 4 publications

References 4 publications

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

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

Ticket price forecasting based on machine learning

A Novel Approach to Predict Flight Fares in India Using SOM-LSSVM Model Approach

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