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A Blending Model Combined DNN and LightGBM for Forecasting the Sales of Airline Tickets

Published: 04 March 2020 Publication History

Abstract

The main goal of this paper is to forecast the sales of airline tickets in time series affected by many factors including different flights, date features and short, middle, long-term historical sales information. The Deep Neural Network (DNN) model and Light Gradient Boosting Machine (LightGBM) model are combined as a Blending model to forecast the sales of airline tickets in future. Simulation results demonstrate the Blending model is better than DNN and LightGBM evaluated by performance metrics including Mean absolute error (MAE) and Mean squared error (MSE).

References

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M. M. Mohie El-Din, M. S. Farag and A. A. Abouzeid, Airline Passenger Forecasting in EGYPT (Domestic and International). International Journal of Computer Applications (0975--8887) Volume 165 - No.6, May 2017.
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S. M. T. F. Ghomi and K. Forghani, "Airline passenger forecasting using neural networks and Box-Jenkins," 2016 12th International Conference on Industrial Engineering (ICIE), Tehran, 2016, pp. 10--13.
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Sun Weiwei. Research and implementation of air revenue management demand forecasting algorithm based on machine learning [D]. Shandong University, 2019.
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Chen, IF. & Lu, CJ. Neural Comput & Applic (2017) 28: 2633. https://doi.org/10.1007/s00521-016-2215-x
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Pavlyshenko, B.M. Machine-Learning Models for Sales Time Series Forecasting. Data 2019, 4, 15.
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Weng, T., Liu, W. and Xiao, J. (2019), "Supply chain sales forecasting based on lightGBM and LSTM combination model", Industrial Management & Data Systems, Vol. ahead-of-print No. ahead-of-print. DOI=https://doi.org/10.1108/IMDS-03-2019-0170
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Polikar R. (2012) Ensemble Learning. In: Zhang C., Ma Y. (eds) Ensemble Machine Learning. Springer, Boston, MA
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Ke, Guolin, Meng, Qi, Finley, Thomas, Wang, Taifeng, Chen, Wei, Ma, Weidong, Ye, Qiwei, Liu, Tie-Yan, 2017. LightGBM: A Highly Efficient Gradient Boosting Decision Tree. In: 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA.

Cited By

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  • (2023)Sales Prediction of Walmart Sales Based on OLS, Random Forest, and XGBoost ModelsHighlights in Science, Engineering and Technology10.54097/hset.v49i.851349(244-249)Online publication date: 21-May-2023
  • (2021)Stock Price Prediction Based on XGBoost and LightGBME3S Web of Conferences10.1051/e3sconf/202127501040275(01040)Online publication date: 21-Jun-2021

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  1. A Blending Model Combined DNN and LightGBM for Forecasting the Sales of Airline Tickets

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      CSAI '19: Proceedings of the 2019 3rd International Conference on Computer Science and Artificial Intelligence
      December 2019
      370 pages
      ISBN:9781450376273
      DOI:10.1145/3374587
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      • Shenzhen University: Shenzhen University

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      Published: 04 March 2020

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      Author Tags

      1. Airline tickets
      2. DNN
      3. Ensemble learning
      4. LightGBM
      5. Model Blending
      6. Sales forecasting

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      • (2023)Sales Prediction of Walmart Sales Based on OLS, Random Forest, and XGBoost ModelsHighlights in Science, Engineering and Technology10.54097/hset.v49i.851349(244-249)Online publication date: 21-May-2023
      • (2021)Stock Price Prediction Based on XGBoost and LightGBME3S Web of Conferences10.1051/e3sconf/202127501040275(01040)Online publication date: 21-Jun-2021

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