Title: Forecasting foreign tourist arrivals in India using a single time series approach based on rough set theory

Authors: Kriti Kumari; Haresh Kumar Sharma; Shalini Chandra; Samarjit Kar

Addresses: Department of Mathematics and Statistics, Banasthali Vidyapith, Jaipur, 304022, Rajasthan, India ' Department of Mathematics, Shree Guru Gobind Singh Tricentenary University, Gurugram, 122505, India ' Department of Mathematics and Statistics, Banasthali Vidyapith, Jaipur, 304022, Rajasthan, India ' Department of Mathematics National Institute of Technology Durgapur, West Bengal, 713209, India

Abstract: In this study, a hybrid approach based on single forecasts and rough set theory (RST) is proposed for forecasting foreign tourist arrivals (FTAs) to India. In the formulation of the proposed hybrid method, the FTAs time series data is first forecasted using four time series models: Naive I, Naive II, Grey, and vector error correction (VEC) models. Then the RST is applied to generate an appropriate weight coefficient and the single forecasting results are combined via the weight coefficient. The study also compares the forecasting results of the hybrid method with single forecasts and other combination methods such as the simple average (SA) and the inverse of the mean absolute percentage error (IMAPE). Empirical results show that the proposed hybrid approach performs better than the other single forecasting models.

Keywords: hybrid approach; time series forecasting; tourist arrivals; single forecasts; rough set; seasonality; multivariate models; weight coefficient.

DOI: 10.1504/IJCSM.2022.128652

International Journal of Computing Science and Mathematics, 2022 Vol.16 No.4, pp.340 - 354

Received: 14 May 2020
Accepted: 10 Sep 2020
Published online: 01 Feb 2023 *

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