Airline Fare Prediction Using Machine Learning Algorithms

Subramanian, R. Raja; Murali, Marisetty Sai; Deepak, B; Deepak, Prudhivi; Reddy, Hamsinipally Nikhil; Sudharsan, R. Raja

doi:10.1109/icssit53264.2022.9716563

Cited by 8 publications

(4 citation statements)

References 14 publications

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“…In [22], Joshi et al adopted a similar approach with fewer ML models, by investigating new features, like flight duration, and achieved up to 90% prediction score. In [23] feature selection algorithms were applied along with hyperparameter methods to find the optimal model parameters and set of features for flight description in order to predict airfare price prediction. In [24] explainability for the problem under study has been introduced towards a deeper insight into the models that could provide an efficient solution, in order to give robust and explainable predictions.…”

Section: Related Workmentioning

confidence: 99%

A Holistic Approach on Airfare Price Prediction Using Machine Learning Techniques

et al. 2023

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Globalization of markets involves new strategies and price policies from professionals that contribute to global competitiveness. Airline companies are changing tickets' prices very often considering a variety of factors based on their proprietary rules and algorithms that are searching for the most suitable price policy. Recently, Artificial Intelligence (AI) models are exploited for the latter task, due to their compactness, fast adaptability, and many potentials in data generalization. This paper represents an analysis of airfare price prediction towards finding similarities in the pricing policies of different Airline companies by using AI Techniques. More specifically, a set of effective features is extracted from 136.917 data flights of Aegean, Turkish, Austrian and Lufthansa Airlines for six popular international destinations. The extracted set of features is then used to conduct a holistic analysis from the perspective of the end user who seeks the most affordable ticket cost, considering a destination-based evaluation including all airlines, and an airline-based evaluation including all destinations. For the latter cause, AI models from three different domains and a total of 16 model architectures are considered to resolve the airfare price prediction problem: Machine Learning (ML) with eight state-of-the-art models, Deep Learning (DL) with six CNN models and Quantum Machine Learning (QML) with two models. Experimental results reveal that at least three models from each domain, ML, DL, and QML, are able to achieve accuracies between 89% and 99% in this regression problem, for different international destinations and airline companies.

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Section: Related Workmentioning

confidence: 99%

A Holistic Approach on Airfare Price Prediction Using Machine Learning Techniques

et al. 2023

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“…However, forecasting airfares continues to pose significant difficulties [4,6]. The dynamics of travel patterns are characterized by continual fluctuations, which ultimately impact the variability of ticket prices [7]. Airlines face the challenge of striking a balance between maximizing the profits and managing the changes of demand since the travelers are constantly seeking for cost-effective options and availability [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

A Multi-Aspect Informed GRU: A Hybrid Model of Flight Fare Forecasting with Sentiment Analysis

Degife,

Lin

2024

Applied Sciences

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This paper presents an advanced method for forecasting flight fares that combines aspect-based sentiment analysis (ABSA) with deep learning techniques, particularly the gated recurrent unit (GRU) model. This approach leverages historical airline ticket transaction data and customer reviews to better understand airline fare dynamics and the impact of customer sentiments on pricing. The aspect analysis extracts key service aspects from customer feedback and provides insightful correlations with airfare. These were further categorized into nine groups for sensitivity analysis, which offered a deeper understanding of how each group influences customers’ attitudes. This ABSA-driven forecasting method marks a departure from traditional models by utilizing sentiment data alongside airline transaction data to improve the predictive accuracy. Its effectiveness is demonstrated through metrics including a root mean square error (RMSE) of 0.0071, a mean absolute error (MAE) of 0.0137, and a coefficient of determination (R2) of 0.9899. Additionally, this model shows strong prediction performance in both short- and long-term fare predictions. It not only advances airfare forecasting methods but provides valuable insights for decision makers of airline industry to refine the pricing strategies or make improvements when it is indicated some services require further attention.

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“…RNN-based models have been successfully applied to the predictions of future prices [20], and long short-term memory (LSTM) and gated recurrent unit (GRU) are the top options [18,21]. They can also be used together with conventional ML-based prediction models, such as decision trees, genetic algorithms [22], and support vector machines [23], for the prediction of prices. Notice that the trend exhibited by the flight ticket fare is highly complex and nonlinear [24].…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, a lack of appropriate data is a major impediment to the advancement of studies on projecting future fares of flight tickets [22]. Since ticket prices are extremely sensitive to business interests, they are seldom disclosed, and the vast majority of airlines do not make their pricing plans known to the public.…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning-Powered GRU Model for Flight Ticket Fare Forecasting

Degife

Lin

2023

Applied Sciences

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Forecasting flight fares is a critical task in the rapidly expanding civil aviation industry and involves numerous factors. However, traditional airfare prediction systems are ineffective due to the complex and nonlinear relationships of multiple factors, which are not able to accurately account for the impact of different attributes such as time period. To tackle these issues, in this study, we proposed a novel approach that utilizes a deep-learning model, specifically, the Gated Recurrent Unit (GRU), by incorporating 44 decision features. The proposed model is able to capture the intricate relationships between various factors effectively and predict air ticket prices with high accuracy. In the experiments, it was found that the GRU model significantly outperforms not only classic machine learning models but also the MLP and LSTM in terms of assessment indicators of mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). The GRU model is thus promising concerning the fare prediction of flight tickets.

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Airline Fare Prediction Using Machine Learning Algorithms

Cited by 8 publications

References 14 publications

A Holistic Approach on Airfare Price Prediction Using Machine Learning Techniques

A Holistic Approach on Airfare Price Prediction Using Machine Learning Techniques

A Multi-Aspect Informed GRU: A Hybrid Model of Flight Fare Forecasting with Sentiment Analysis

Deep-Learning-Powered GRU Model for Flight Ticket Fare Forecasting

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