Modeling the occupancy rate and nights spent at tourism accommodations of Spain using autoregressive deep learning networks

Abstract

Purpose

The purpose of this paper is to autoregressively model the net occupancy rate of beds and bedrooms in hotels and similar accommodations and the nights spent at these accommodations of Spain for the period of 1990–2023 using monthly data.

Design/methodology/approach

The monthly occupancy rate of hotels and the total number of hotel nights data of Spain for the 1990M01–2023M09 range is considered. An autoregressive deep learning network is developed for the modeling of both metrics. Moreover, the results of the proposed autoregressive deep learning method are compared to those of a classical artificial neural network.

Findings

The actual occupancy rate, total night data and the deep learning model results are compared showing the accuracy of the developed model. Moreover, the R², mean absolute error, root mean square error and mean absolute percentage error of the models are calculated further demonstrating the high performance of the developed model. The R² values higher than 0.9 are achieved for both occupancy rate and total number of hotel nights data.

Practical implications

The modeling results given in this paper demonstrate that the previous values of the net occupancy rate and the total number of nights can be used as inputs of a deep learning network model by which accurate forecasts can be made for the future values of the occupancy rate and the total number of hotel nights. This modeling approach possesses importance from the practical viewpoint considering that the accurate planning and forecast of the net occupancy rate and the total number of nights affect the tourism income.

Originality/value

This study differs from existing literature by attempting to model the occupancy rate and the total number of hotel nights data autoregressively using deep learning networks.

研究目的

本研究旨在通过自回归方式对西班牙酒店及类似住宿的床位和房间的净入住率以及这些住宿的过夜天数进行建模, 时间范围为1990年至2023年, 使用月度数据。

研究方法

本研究使用了1990年1月至2023年9月期间西班牙酒店的月度入住率和总过夜天数数据, 开发了一个用于这两个指标建模的自回归深度学习网络。此外, 提出的自回归深度学习方法的结果与经典的人工神经网络进行了对比。

研究发现

实际的入住率和总过夜天数数据与深度学习模型的结果进行了对比, 显示出所开发模型的准确性。此外, 计算了模型的R²、MAE、RMSE和MAPE, 进一步证明了所开发模型的高性能。对于入住率和总过夜天数数据, R²值均超过0.9。

研究创新

本研究与现有文献的不同之处在于, 尝试使用深度学习网络自回归建模入住率和总过夜天数数据。

实践意义

本文给出的建模结果表明, 净入住率和总过夜天数的先前值可作为深度学习网络模型的输入, 进而对未来的入住率和总过夜天数进行准确预测。考虑到净入住率和总过夜天数的准确规划和预测会影响旅游收入, 从实际角度来看, 这种建模方法具有重要意义。

Citation

Sürekçi Yamaçlı, D. (2025), "Modeling the occupancy rate and nights spent at tourism accommodations of Spain using autoregressive deep learning networks", Journal of Hospitality and Tourism Technology, Vol. 16 No. 2, pp. 305-325. https://doi.org/10.1108/JHTT-12-2023-0424