Tourist Arrivals Demand Forecasting Using Rough Set-Based Time Series Models
DOI:
https://doi.org/10.31181/dma31202567Keywords:
Foreign tourist arrivals, Forecasting, Rough set theory, Time series modelAbstract
This paper uses different univariate and multivariate autoregression and soft computing models for forecasting qualitative time series data. Rough set theory (RST) is one of the most convenient soft computing methods to investigate the imprecision and ambiguity of an information table by using qualitative dependent and independent variables. Moreover, applications based on rough set theory are used for the classification problem. The rough set is quite different from the other statistical and machine learning data analysis approaches based on mathematical equations. The empirical analysis indicates that the rough set is a highly accurate soft computing model for forecasting FTA data.
Downloads
References
Dritsaki, N. (2004). Cointegration analysis of German and British tourism demand for Greece, Tourism Management, 25(1), 111-119. https://10.1016/S0261-5177(03)00061-X
Kulendran, N. (1996). Modelling quarterly tourist flows to Australia using Cointegration analysis. Tourism Economics, 2(3), 203-222. https://doi.org/10.1177/135481669600200301
Kulendran, N. and King, M. L. (1997). Forecasting international quarterly tourist flows using error-correction and time series models, International Journal of Forecasting, 13(3), 319-327. https://doi.org/10.1016/S0169-2070(97)00020-4
Pawlak, Z. (1982). Rough sets, International Journal of Computer and Information Science, 11, 341-356. https://doi.org/10.1007/BF01001956
Celotto, E., Ellero, A. and Ferretti, P. (2012). Short-medium term tourist services demand forecasting with Rough Set Theory, Procedia Economics and Finance, 3, 62-67. https://10.1016/S2212-5671(12)00121-9
Li, J., Li F., and Zhou, G. (2011). Travel Demand Prediction in Tangshan City of China Based on Rough Set, Springer-Verlag Berlin Heidelberg, 440-446. https://doi.org/10.1007/978-3-642-25255-6_56
Singh, A., Singh, A., Sharma, H. K., & Majumder, S. (2023). Criteria selection of housing loan based on dominance-based rough set theory: An Indian case. Journal of Risk and Financial Management, 16(7), 309. https://doi.org/10.3390/jrfm16070309
Pamučar, D., Stević, Ž., & Zavadskas, E. K. (2018). Integration of interval rough AHP and interval rough MABAC methods for evaluating university web pages. Applied soft computing, 67, 141-163. https://doi.org/10.1016/j.asoc.2018.02.057
Majumder, S., Singh, A., Singh, A., Karpenko, M., Sharma, H. K., & Mukhopadhyay, S. (2024). On the analytical study of the service quality of Indian Railways under soft-computing paradigm. Transport, 39(1), 54-63. https://doi.org/10.3846/transport.2024.21385
Sharma, H. K., & Kar, S. (2018). Decision making for hotel selection using rough set theory: A case study of Indian hotels. International Journal of Applied Engineering Research, 13(6), 3988-3998.
Karavidic, Z., & Projovic, D. (2018). A multi-criteria decision-making (MCDM) model in the security forces operations based on rough sets. Decision Making: Applications in Management and Engineering, 1(1), 97-120. https://doi.org/10.31181/dmame180197k
Sagar, P., Gupta, P., & Tanwar, R. (2021). A novel prediction algorithm for multivariate data sets. Decision Making: Applications in Management and Engineering, 4(2), 225-240. https://doi.org/10.31181/dmame210402215s
Faustino, P.C., Pinheiro, A.C., Carpinteiro, A.O. and Lima, I. (2011). Time series forecasting through rule based models obtained via rough sets, Artificial Intelligence Review, 36, 299-310. https://doi.org/10.1007/s10462-011-9215-0
Law, R. and Au, N. (2000). Relationship modelling in tourism shopping: a decision rules induction approach, Tourism Management, 21, 241-249. https://doi.org/10.1016/S0261-5177(99)00056-4
Goh, C., Law, R. and Mok, H. M. K. (2008). Analyzing and forecasting tourism demand: A rough sets approach, Journal of Travel Research, 46, 327-338. https://doi.org/10.1177/0047287506304
Hawau’u W-Y, Samuel OA, Gabriel OB, Bamidele FO (2022). Assessment of passengers’ satisfaction on service quality of Arik airline at Nnamdi Azikwe international airport, Abuja, Nigeria. Indian Journal of Engineering, 19(51), 43-54.
Xiaoya, H., & Zhiben, J. (2011). Research on econometric model for domestic tourism income based on rough set. In Innovative Computing and Information: International Conference, ICCIC 2011, Wuhan, China, September 17-18, 2011. Proceedings, Part II (pp. 259-266). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-23998-4_37
Goh, C. and Law, R. (2003). Incorporating the rough sets theory into travel demand analysis, Tourism Management, 24, 511-517. https://doi.org/10.1016/S0261-5177(03)00009-8
Socio-economic statistical information, http://www.indiastat.com. Accessed 12 December 2023.
Julong, D. (2002). Grey prediction and grey decision. Huazhong University of Science and Technology Press, Wuhan, 71-75.
Kumari, K., Sharma, H. K., Chandra, S., & Kar, S. (2022). Forecasting foreign tourist arrivals in India using a single time series approach based on rough set theory. International Journal of Computing Science and Mathematics, 16(4), 340-354. https://doi.org/10.1504/IJCSM.2022.128652
Agboola Olasunkanmi J, Roland Uhunmwangho, A. Big-Alabo, Esoasa Omorogiuwa (2021). Artificial Neural Network for Long-term Industrial Load Forecast: Trans-Amadi Industrial Layout, Nigeria. Indian Journal of Engineering, 18(49), 212-221
Pawlak, Z. (1991). Rough sets, A Theoretical Aspect of Reasoning about data, Kluwer Academic Publisher, Boston. https://doi.org/10.1007/978-94-011-3534-4
Predki, B., Słowiński, R., Stefanowski, J., Susmaga, R., & Wilk, S. (1998). ROSE-software implementation of the rough set theory. In Rough Sets and Current Trends in Computing: First International Conference, RSCTC’98 Warsaw, Poland, June 22–26, 1998 Proceedings 1 (pp. 605-608). Springer Berlin Heidelberg. https://doi.org/10.1007/3-540-69115-4_85
Engle, R.F. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of the variance of United Kingdom Ination. Econometrica 50(4): 987-1008. https://doi.org/10.2307/1912773
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Scientific Oasis

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.