Bayesian BILSTM approach for tourism demand forecasting

Anurag Kulshrestha, Venkataraghavan Krishnaswamy and Mayank Sharma

Annals of Tourism Research, 2020, vol. 83, issue C

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.

Keywords: Tourism demand forecasting; BiLSTM; LSTM; SVR; RBFNN; Bayesian optimization (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (19)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0160738320300694
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:anture:v:83:y:2020:i:c:s0160738320300694

DOI: 10.1016/j.annals.2020.102925

Access Statistics for this article

Annals of Tourism Research is currently edited by John Tribe

More articles in Annals of Tourism Research from Elsevier
Bibliographic data for series maintained by Catherine Liu ().