Factor Models for High-Dimensional Time Series Forecasting: An Application to Revenue Management

DOI

10.2991/978-94-6463-042-8_178How to use a DOI?

Keywords

time series; revenue management; forecasting; flight booking rates

Abstract

Since the 2020s, the emergence of the COVID-19 pandemic has brought the development of transportation, tourism, and entertainment industries to a standstill. The idea of revenue management (RM) improved the profitability for different types of companies. Therefore, establishing a good RM model and accurate mathematical forecasting model is particularly important for struggling airlines. We herein propose a factor model based on high-dimensional time series that can efficiently use continuous time historical data and the related environmental historical data to predict the passenger load factor. Therefore, accurate and effective dimensional reduction and feature expression of highdimensional matrix time series have profound practical significance for studying time series data. To verify the efficacy of the model and parameter estimation methods, we applied them to the booking rates of 11 flights over 365 days (year 2018). After experimental analysis and comparison tests with other methods studied in the paper has the best effect and the results of comparisons with different dimensions indicate that the error rate of the proposed method is less than 0.1.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Ruixue Li
AU  - Chaofeng Yuan
AU  - Nan Wang
PY  - 2022
DA  - 2022/12/29
TI  - Factor Models for High-Dimensional Time Series Forecasting: An Application to Revenue Management
BT  - Proceedings of the 2022 International Conference on mathematical statistics and economic analysis (MSEA 2022)
PB  - Atlantis Press
SP  - 1247
EP  - 1251
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-042-8_178
DO  - 10.2991/978-94-6463-042-8_178
ID  - Li2022
ER  -