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A Temporal Convolutional Network for Hotel Demand Prediction Based on NSGA3 Feature Selection

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2024, 29(10), pp.121-128
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : September 27, 2024
  • Accepted : October 18, 2024
  • Published : October 31, 2024

Keehyun Park 1 Gyeongho Jung 1 Hyunchul Ahn 1

1국민대학교

Accredited

ABSTRACT

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.

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