Purpose

This paper aims to propose a hybrid-forecasting model for long-term tourism demand forecasting. As such, it attempts to model the tourism demand in the Philippines, which is a relatively underrepresented area in the literature, despite its tourism sector’s growing economic progress.

Design/methodology/approach

A hybrid support vector regression (SVR) – seasonal autoregressive integrated moving averages (SARIMA) model is proposed to model the seasonal, linear and nonlinear components of the tourism demand in a destination country. The paper further proposes the use of multiple criteria decision-making (MCDM) approaches in selecting the best forecasting model among a set of considered models. As such, a preference ranking organization method for enrichment of evaluations (PROMETHEE) II is used to rank the considered forecasting models.

Findings

The proposed hybrid SVR-SARIMA model is the best performing model among a set of considered models in this paper using performance criteria that evaluate the errors of magnitude, directionality and trend change, of a forecasting model. Moreover, the use of the MCDM approach is found to be a relevant and prospective approach in selecting the best forecasting model among a set of models.

Originality/value

The novelty of this paper lies in several aspects. First, this paper pioneers the demonstration of the SVR-SARIMA model’s capability in forecasting long-term tourism demand. Second, this paper is the first to have proposed and demonstrated the use of an MCDM approach for performing model selection in forecasting. Finally, this paper is one of the very few papers to provide lenses on the current status of Philippine tourism demand.

中文翻译：

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使用PROMETHEE II作为选择方法的旅游预测混合SVR-SARIMA模型：菲律宾

目的

本文旨在提出一种用于长期旅游需求预测的混合预测模型。因此，尽管菲律宾旅游业的经济发展日新月异，但它还是试图对菲律宾的旅游需求进行建模，菲律宾在文献中是一个相对不足的领域。

设计/方法/方法

提出了一种混合支持向量回归（SVR）–季节性自回归综合移动平均值（SARIMA）模型，以对目的地国家旅游需求的季节性，线性和非线性成分进行建模。本文还提出了使用多准则决策（MCDM）方法在一组考虑的模型中选择最佳预测模型的方法。这样，用于评估的优先级排序组织方法（PROMETHEE）II用于对考虑的预测模型进行排序。

发现

所提出的混合SVR-SARIMA模型是本文中一组考虑模型中性能最好的模型，其中使用性能标准来评估预测模型的幅度，方向性和趋势变化的误差。此外，在选择一组模型中的最佳预测模型时，发现使用MCDM方法是一种相关且有前途的方法。

创意/价值

本文的新颖之处在于几个方面。首先，本文率先演示了SVR-SARIMA模型在预测长期旅游需求中的能力。其次，本文是第一个提出并演示了MCDM方法在预测中进行模型选择的方法。最后，这篇论文是提供有关菲律宾旅游需求当前状况的极少数论文之一。