This paper proposed a hybrid intelligent approach based on empirical mode decomposition (EMD), autoregressive integrated moving average (ARIMA) and Monte Carlo simulation (MCS) methods for multi-step ahead medical tourism (MT) forecasting using explanatory input variables based on two decade real-time recorded database. In the proposed hybrid model, these variables are 1st extracted then medical tourism is forecasted to perform the long term as well as the short term goal and planning in the nation. The multi-step ahead medical tourism is forecasted recursively, by utilizing the 1st forecasted value as the input variable to generate the next forecasting value and this procedure is continued till third step ahead forecasted value. The proposed approach firstly tested and validated by using international tourism arrival (ITA) dataset then proposed approach is implemented for forecasting of medical tourism arrival in nation. In order to validate the performance and accuracy of the proposed hybrid model, a comparative analysis is performed by using Monte Carlo method and the results are compared. Obtained results shows that the proposed hybrid forecasting approach for medical tourism has outperformance characteristics.

中文翻译：

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结合EMD，ARIMA和Monte Carlo的混合方法进行多步超前医疗旅游预测

本文提出了一种基于经验模式分解（EMD），自回归综合移动平均（ARIMA）和蒙特卡罗模拟（MCS）方法的混合智能方法，该方法使用基于两个十年的解释性输入变量进行多步超前医疗旅游（MT）预测实时记录的数据库。在提出的混合模型中，首先提取这些变量，然后预测医疗旅游将在全国范围内执行长期，短期目标和计划。通过将第一个预测值作为输入变量以生成下一个预测值，对多步提前医疗旅游进行递归预测，然后继续执行此过程，直到提前三步预测值为止。该方法首先利用国际旅游入境量（ITA）数据集进行了测试和验证，然后将该方法用于全国医疗旅游量的预测。为了验证所提出的混合模型的性能和准确性，使用蒙特卡洛方法进行了比较分析，并对结果进行了比较。所得结果表明，提出的医疗旅游混合预测方法具有优异的性能。