利用人工神经网络建模和预测克罗地亚入境旅游需求:比较研究

IF 3.1 Q2 HOSPITALITY, LEISURE, SPORT & TOURISM
M. Çuhadar
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引用次数: 4

摘要

旅游需求是所有与旅游有关的商业决策最终依赖的基础。准确估计旅游需求对旅游业至关重要,因为它可以帮助减少风险和不确定性,并有效地为更好的旅游规划提供基础信息。本研究的目的是开发最优的预测模型,在与三种不同方法的预测性能相比,即人工神经网络(ANN),指数平滑和Box-Jenkins方法,用于预测克罗地亚每月入境游客流量。以往的研究已经应用于基于时间序列模型和随机方法的克罗地亚旅游需求预测。然而,利用人工神经网络进行月度和比较旅游需求预测的研究仍然有限,本文旨在填补这一空白。利用2005年1月至2019年12月期间克罗地亚每月外国游客入境人数的数据,建立了最优预测模型。模型的预测性能用平均绝对百分比误差(MAPE)统计量来衡量。实验结果表明,与各种模型的预测性能相比,在本研究应用的所有模型中,具有[4-3-1]结构的12个滞后人工神经网络模型表现最好。结合本研究的实证结果和以往对旅游预测的研究,可以看出,与传统统计方法的预测结果相比,不存在任何负性(如过度训练、错误架构等)的人工神经网络模型的预测结果是成功的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modelling and Forecasting Inbound Tourism Demand to Croatia using Artificial Neural Networks: A Comparative Study
Tourism demand is the basis on which all commercial decisions concerning tourism ultimately depend. Accurate estimation of tourism demand is essential for the tourism industry because it can help reduce risk and uncertainty as well as effectively provide basic information for better tourism planning. The purpose of this study is to develop the optimal forecasting model that yields the highest accuracy when compared to the forecast performances of three different methods, namely Artificial Neural Network (ANN), Exponential Smoothing, and Box-Jenkins methods for forecasting monthly inbound tourist flows to Croatia. Prior studies have been applied to forecast tourism demand to Croatia based on time series models and casual methods. However, the monthly and comparative tourism demand forecasting studies using ANNs are still limited, and this paper aims to fill this gap. The number of monthly foreign tourist arrivals to Croatia covers the period between January 2005-December 2019 data were used to build optimal forecasting models. Forecasting performances of the models were measured by Mean Absolute Percentage Error (MAPE) statistics. As a result of the experiments carried out, when compared to the forecasting performances of various models, 12 lagged ANN models, which have [4-3-1] architecture, were seen to perform best among all models applied in this study. Considering both the empirical findings obtained from this study and previous studies on tourism forecasting, it can be seen that ANN models that do not have any negativities (such as over-training, faulty architecture, etc.) produce successful forecasting results when compared with results generated by conventional statistical methods.
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来源期刊
Journal of Tourism and Services
Journal of Tourism and Services HOSPITALITY, LEISURE, SPORT & TOURISM-
CiteScore
7.80
自引率
16.70%
发文量
14
期刊介绍: Journal of Tourism and Services, established in September 2010, is the international reviewed scientific research journal published by the Center for International Scientific Research of VŠO and VŠPP in cooperation with the following partners. The journal publishes high-quality scientific papers and essays with a focus on tourism and service industry development. Together with the scientific part and in order to promote the exchange of current and innovative ideas and stimulating debate, the Journal also includes Reviews of Existing Work or Short Essays, Research Notes, and Research and Industry sections to address important topics and advance theoretical knowledge or thinking about key areas of tourism and services and to allow researchers to present initial findings and reflections or problems concerning fieldwork and research in general.
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