对入境旅游需求进行科学合理的预测直接关系到中国入境旅游发展战略的制定和实施,具有积极的现实意义。目前,BP神经网络作为一种常见的传统机器学习方法,被广泛用于旅游需求预测建模。然而,由于BP神经网络存在诸如易过配、参数设置难、获得全局最优解难等局限,在实际应用中表现极不稳定。有鉴于此,拟将BP神经网络和集成学习技术相结合,构建入境旅游需求预测的神经网络集成模型,并对美国、英国、澳大利亚3个客源国近20 a来的入境游客量数据进行验证分析。结果表明,神经网络集成有效克服了单个BP神经网络在小规模数据集上的局限性,获得了比包括BP神经网络在内的传统机器学习技术和传统统计方法更为准确的预测结果,这有利于更加准确地把握中国入境旅游市场需求。
Accurate prediction of the tourism demand is important for tourism management. The state-of-art methods for predictive modeling used in tourism research include traditional statistical methods, soft computing methods, and artificial intelligence methods. Note that artificial intelligence methods, which were introduced to tourism research in the 1990s, have greatly improved the predictive accuracy of modeling methods. Machine learning is an important area of artificial intelligence, which has been widely recognized as a powerful tool for intelligent data analysis. BP neural network, as one of the traditional machine learning technique, has been widely used to construct predictive model for intelligent data analysis. However, BP neural network suffers from several drawbacks, such as overfitting, difficulties in setting parameters, and local minima problem, and hence the performance of BP neural network is very unstable in practical applications. This paper combines an advanced machine learning paradigm named ensemble learning with BP neural network to build neural network ensemble for tourism demand prediction. This study conducts predictive modeling for tourism demand of three important tourist source countries of US, Britain and Australia for travel to Chinese mainland. The results show that, by combining a number of diverse neural networks, neural network ensemble significantly improves the predictive accuracy over traditional statistical methods and traditional machine learning methods including single BP neural network. Such method provides a better choice for more accurate predictive modeling for tourism demand.
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