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Bayesian BILSTM approach for tourism demand forecasting

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  • Kulshrestha, Anurag
  • Krishnaswamy, Venkataraghavan
  • Sharma, Mayank

Abstract

The tourism sector, with its perishable nature of products, requires precise estimation of demand. To this effect, we propose a deep learning methodology, namely Bayesian Bidirectional Long Short-Term Memory (BBiLSTM) network. BiLSTM is a deep learning model, and Bayesian optimization is utilized to optimize the hyperparameters of this model. Five experiments using the tourism demand data of Singapore are conducted to ascertain the validity and benchmark the proposed BBiLSTM model. The experimental findings suggest that the BBiLSTM model outperforms other competing models like Long Short-Term Memory (LSTM) network, Support Vector Regression (SVR), Radial Basis Function Neural Network (RBFNN) and Autoregressive Distributed Lag Model (ADLM). The study contributes to tourism literature by proposing a superior deep-learning method for demand forecasting.

Suggested Citation

  • Kulshrestha, Anurag & Krishnaswamy, Venkataraghavan & Sharma, Mayank, 2020. "Bayesian BILSTM approach for tourism demand forecasting," Annals of Tourism Research, Elsevier, vol. 83(C).
    • Handle: RePEc:eee:anture:v:83:y:2020:i:c:s0160738320300694
    DOI: 10.1016/j.annals.2020.102925
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    12. Ming Yin & Feiya Lu & Xingxuan Zhuo & Wangzi Yao & Jialong Liu & Jijiao Jiang, 2024. "Prediction of daily tourism volume based on maximum correlation minimum redundancy feature selection and long short‐term memory network," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 344-365, March.
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