@article{ART003130964},
author={Keehyun Park and Gyeongho Jung and Hyunchul Ahn},
title={A Temporal Convolutional Network for Hotel Demand Prediction Based on NSGA3 Feature Selection},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2024},
volume={29},
number={10},
pages={121-128},
doi={10.9708/jksci.2024.29.10.121}
TY - JOUR
AU - Keehyun Park
AU - Gyeongho Jung
AU - Hyunchul Ahn
TI - A Temporal Convolutional Network for Hotel Demand Prediction Based on NSGA3 Feature Selection
JO - Journal of The Korea Society of Computer and Information
PY - 2024
VL - 29
IS - 10
PB - The Korean Society Of Computer And Information
SP - 121
EP - 128
SN - 1598-849X
AB - Demand forecasting is a critical element of revenue management in the tourism industry. Since the 2010s, with the globalization of the tourism industry and the increase of different forms of marketing and information sharing, such as SNS, forecasting has become difficult due to non-linear activities and unstructured information. Various forecasting models for resolving the problems have been studied, and ML models have been used effectively. In this study, we applied the feature selection technique (NSGA3) to time series models and compared their performance. In hotel demand forecasting, it was found that the TCN model has a high forecasting performance of MAPE 9.73% with a performance improvement of 7.05% compared to no feature selection. The results of this study are expected to be useful for decision support through improved forecasting performance.
KW - Hotel Demand Forecasting;Time Series;Feature Selection;NSGA3;TCN
DO - 10.9708/jksci.2024.29.10.121
ER -
Keehyun Park, Gyeongho Jung and Hyunchul Ahn. (2024). A Temporal Convolutional Network for Hotel Demand Prediction Based on NSGA3 Feature Selection. Journal of The Korea Society of Computer and Information, 29(10), 121-128.
Keehyun Park, Gyeongho Jung and Hyunchul Ahn. 2024, "A Temporal Convolutional Network for Hotel Demand Prediction Based on NSGA3 Feature Selection", Journal of The Korea Society of Computer and Information, vol.29, no.10 pp.121-128. Available from: doi:10.9708/jksci.2024.29.10.121
Keehyun Park, Gyeongho Jung, Hyunchul Ahn "A Temporal Convolutional Network for Hotel Demand Prediction Based on NSGA3 Feature Selection" Journal of The Korea Society of Computer and Information 29.10 pp.121-128 (2024) : 121.
Keehyun Park, Gyeongho Jung, Hyunchul Ahn. A Temporal Convolutional Network for Hotel Demand Prediction Based on NSGA3 Feature Selection. 2024; 29(10), 121-128. Available from: doi:10.9708/jksci.2024.29.10.121
Keehyun Park, Gyeongho Jung and Hyunchul Ahn. "A Temporal Convolutional Network for Hotel Demand Prediction Based on NSGA3 Feature Selection" Journal of The Korea Society of Computer and Information 29, no.10 (2024) : 121-128.doi: 10.9708/jksci.2024.29.10.121
Keehyun Park; Gyeongho Jung; Hyunchul Ahn. A Temporal Convolutional Network for Hotel Demand Prediction Based on NSGA3 Feature Selection. Journal of The Korea Society of Computer and Information, 29(10), 121-128. doi: 10.9708/jksci.2024.29.10.121
Keehyun Park; Gyeongho Jung; Hyunchul Ahn. A Temporal Convolutional Network for Hotel Demand Prediction Based on NSGA3 Feature Selection. Journal of The Korea Society of Computer and Information. 2024; 29(10) 121-128. doi: 10.9708/jksci.2024.29.10.121
Keehyun Park, Gyeongho Jung, Hyunchul Ahn. A Temporal Convolutional Network for Hotel Demand Prediction Based on NSGA3 Feature Selection. 2024; 29(10), 121-128. Available from: doi:10.9708/jksci.2024.29.10.121
Keehyun Park, Gyeongho Jung and Hyunchul Ahn. "A Temporal Convolutional Network for Hotel Demand Prediction Based on NSGA3 Feature Selection" Journal of The Korea Society of Computer and Information 29, no.10 (2024) : 121-128.doi: 10.9708/jksci.2024.29.10.121
