Hybrid approach combining EMD, ARIMA and monte carlo for multi-step ahead medical tourism forecasting

Issue title: Digital transformation through advances in artificial intelligence and machine learning

Guest editors: Hasmat Malik, Gopal Chaudhary and Smriti Srivastava

Article type: Research Article

Authors: Fatema, Nuzhat^{a; b} | Malik, Hasmat^c | Abd Halim, Mutia Sobihah^{a; *}

Affiliations: [a] Faculty of Business and Management, Universiti Sultan Zainal Abidin (UniSZA), Malaysia | [b] Intelligent Prognostic Private Limited, India | [c] BEARS, CREATE Tower, University Town, NUS Campus, Singapore

Correspondence: [*] Corresponding author. Mutia Sobihah Abd Halim, E-mails: [email protected], [email protected].

Abstract: 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 is 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 show that the proposed hybrid forecasting approach for medical tourism has outperforming characteristics.

Keywords: ARIMA model, explanatory feature, multi-step ahead, medical tourism forecasting, Monte Carlo simulation, feature extraction

DOI: 10.3233/JIFS-189785

Journal: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 2, pp. 1235-1251, 2022