Deep learning models for forecasting aviation demand time series

Kanavos, Andreas; Kounelis, Fotios; Iliadis, Lazaros; Makris, Christos

doi:10.1007/s00521-021-06232-y

Cited by 35 publications

(16 citation statements)

References 27 publications

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“…Li used the SARIMA model and LSTM neural network for prediction, respectively, and the LSTM model was better in predicting the passenger traffic of civil aviation [8]. Kanavos et al [9] developed an air travel demand estimation and forecasting model using the classical autoregressive integrated moving average (ARIMA), the seasonal approach (SARIMA), and a deep learning neural network (DLNN). In addition, many scholars [10][11][12][13][14] have also used the ARIMA model to forecast the passenger traffic of civil aviation.…”

Section: Literature Reviewmentioning

confidence: 99%

Civil Aviation Passenger Traffic Forecasting: Application and Comparative Study of the Seasonal Autoregressive Integrated Moving Average Model and Backpropagation Neural Network

Gu,

Guo,

Zhang

et al. 2024

Sustainability

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With the rapid development of China’s aviation industry, the accurate prediction of civil aviation passenger volume is crucial to the sustainable development of the industry. However, the current prediction of civil aviation passenger traffic has not yet reached the ideal accuracy, so it is particularly important to improve the accuracy of prediction. This paper explores and compares the effectiveness of the backpropagation (BP) neural network model and the SARIMA model in predicting civil aviation passenger traffic. Firstly, this study utilizes data from 2006 to 2019, applies these two models separately to forecast civil aviation passenger traffic in 2019, and combines the two models to forecast the same period. Through comparing the mean relative error (MRE), mean square error (MSE), and root mean square error (RMSE), the prediction accuracies of the two single models and the combined model are evaluated, and the best prediction method is determined. Subsequently, using the data from 2006 to 2019, the optimal method is applied to forecast the civil aviation passenger traffic from 2020 to 2023. Finally, this paper compares the epidemic’s impact on civil aviation passenger traffic with the actual data. This paper improves the prediction accuracy of civil aviation passenger volume, and the research results have practical significance for understanding and evaluating the impact of the epidemic on the aviation industry.

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Section: Literature Reviewmentioning

confidence: 99%

Civil Aviation Passenger Traffic Forecasting: Application and Comparative Study of the Seasonal Autoregressive Integrated Moving Average Model and Backpropagation Neural Network

Gu,

Guo,

Zhang

et al. 2024

Sustainability

View full text Add to dashboard Cite

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“…As a result, it is vital to improving decision-making accuracy in fields such as time series forecasting and industries such as aviation. Deep learning techniques have the potential to improve accuracy, and the study [ 56 ] emphasizes the importance of employing the most up-to-date methods in the aviation industry. Iacus et al [ 51 ] demonstrated how aviation demand is analyzed and modeled using traditional statistical models and deep learning techniques.…”

Section: Techniquesmentioning

confidence: 99%

“…• Forecasts for how passenger preferences, booking curves, and fare product restrictions may change in the aftermath of the COVID-19 pandemic • To make better decisions after Covid-19, it is necessary to delve deeper into dynamic and continuous pricing. [ 56 ] 2021 • ARIMA, SARIMA, and DLNN • ARIMA, SARIMA, and DLNN are all compared in depth. • External factors such as population and financial information for each airport’s state were considered.…”

Section: Techniquesmentioning

confidence: 99%

Systematic review of passenger demand forecasting in aviation industry

Zachariah¹,

Sharma

Kumar

2023

Multimed Tools Appl

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Forecasting aviation demand is a significant challenge in the airline industry. The design of commercial aviation networks heavily relies on reliable travel demand predictions. It enables the aviation industry to plan ahead of time, evaluate whether an existing strategy needs to be revised, and prepare for new demands and challenges. This study examines recently published aviation demand studies and evaluates them in terms of the various forecasting techniques used, as well as the advantages and disadvantages of each. This study investigates numerous forecasting techniques for passenger demand, emphasizing the multiple factors that influence aviation demand. It examined the benefits and drawbacks of various models ranging from econometric to statistical, machine learning to deep neural networks, and the most recent hybrid models. This paper discusses multiple application areas where passenger demand forecasting is used effectively. In addition to the benefits, the challenges and potential future scope of passenger demand forecasting were discussed. This study will be helpful to future aviation researchers while also inspiring young researchers to pursue careers in this industry.

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“…Furthermore, the authors in [ 16 ] incorporate deep neural networks for the problem of forecasting aviation demand time series, where they utilized various models and identified the best implementation among several strategies. One of the most recent works exhibits an LSTM-CNN based system for classification [ 17 ].…”

Section: Related Workmentioning

confidence: 99%

Multiclass sentiment analysis on COVID-19-related tweets using deep learning models

Vernikou

Lyras

Kanavos

2022

Neural Comput & Applic

Self Cite

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COVID-19 is an infectious disease with its first recorded cases identified in late 2019, while in March of 2020 it was declared as a pandemic. The outbreak of the disease has led to a sharp increase in posts and comments from social media users, with a plethora of sentiments being found therein. This paper addresses the subject of sentiment analysis, focusing on the classification of users’ sentiment from posts related to COVID-19 that originate from Twitter. The period examined is from March until mid-April of 2020, when the pandemic had thus far affected the whole world. The data is processed and linguistically analyzed with the use of several natural language processing techniques. Sentiment analysis is implemented by utilizing seven different deep learning models based on LSTM neural networks, and a comparison with traditional machine learning classifiers is made. The models are trained in order to distinguish the tweets between three classes, namely negative, neutral and positive.

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Deep learning models for forecasting aviation demand time series

Cited by 35 publications

References 27 publications

Civil Aviation Passenger Traffic Forecasting: Application and Comparative Study of the Seasonal Autoregressive Integrated Moving Average Model and Backpropagation Neural Network

Civil Aviation Passenger Traffic Forecasting: Application and Comparative Study of the Seasonal Autoregressive Integrated Moving Average Model and Backpropagation Neural Network

Systematic review of passenger demand forecasting in aviation industry

Multiclass sentiment analysis on COVID-19-related tweets using deep learning models

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