A rough set approach for forecasting models

Haresh Kumar Sharma; Kriti Kumari; Samarjit Kar

doi:10.31181/dmame2003001s

Authors

Haresh Kumar Sharma Department of Mathematics, National Institute of Technology, Durgapur, India
Kriti Kumari Department of Mathematics, Banasthali University, Jaipur, Rajasthan, India
Samarjit Kar Department of Mathematics, National Institute of Technology, Durgapur, India

DOI:

https://doi.org/10.31181/dmame2003001s

Keywords:

Forecasting, Mean Absolute Percent Error (MAPE), Rough Set, Total Roughness, Maximum Dependency Degree

Abstract

This paper introduces the performance of different forecasting methods for tourism demand, which can be employed as one of the statistical tools for time series forecasting. The Holt-Winters (HW), Seasonal Autoregressive Integrated Moving Average (SARIMA) and Grey model (GM (1, 1)) are three important statistical models in time-series forecasting. This paper analyzes and compare the performance of forecasting models using rough set methods, Total Roughness (TR), Min-Min Roughness (MMR) and Maximum Dependency of attributes (MDA). Current research identifies the best time series forecasting model among the three studied time series forecasting models. Comparative study shows that HW and SARIMA are superior models than GM (1, 1) for forecasting seasonal time series under TR, MMR and MDA criteria. In addition, the authors of this study showed that GM (1, 1) grey model is unqualified for seasonal time series data.

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References

Box, G. E. P., & Jenkins, G. M. (1976). Time Series Analysis: Forecasting and Control, Revised. San Fransisco: Holden-Day, 575.

Celotto, E., Ellero, A. &Ferretti, P. (2012). Short-medium term tourist services demand to forecast with rough ret theory, Procedia Economics, and Finance, 3, 62-67.

Chen, C.-F., Chang,Y.-H &Chang, Y.-W. (2009). Seasonal ARIMA Forecasting of inbound air Travel arrivals to Taiwan, Transportmetrica, 5(2), 125-140.

Chu, F. L. (1998). Forecasting tourism demand in Asian-Pacific countries, Annual of Tourism Research, 25(3), 597-615.

Deng, J. L. (1982). Control problems of grey systems, Systems and Control Letters, 1(1), 288-294.

Faustino, P. C., Pinheiro, A. C., Carpinteiro, A. O. &Lima, I. (2011). Time series forecasting through rule-based models obtained via rough sets. Artif Intell Rev, 36, 299-310.

Goh, C. &Law, R. (2002). Modeling and forecasting tourism demand for arrivals with stochastic nonstationary seasonality and intervention, Tourism Management, 23, 499-510.

Goh, C. & Law, R. (2003). Incorporating the rough sets theory into travel demand analysis, Tourism Management, 24, 511-517.

Golmohammadi, A., & Ghareneh, N. S. (2011). Importance analysis of travel attributes using a rough set-based neural network: The case of Iranian tourism industry Journal of Hospitality and Tourism Technology 2(2), 155-171.

Hassanein, W. A., & Elmelegy, A. (2013). An algorithm for selecting clustering attribute using significance of attributes, International Journal of Database Theory and Application, 5, 53-66.

Herawan, T., Deris, M., & Abawajy, J. (2010a). A rough set approach for selecting clustering Attribute, Knowledge-Based Systems, 23, 220-231.

Herawan, T., Ghazali, R., & Deris, M. (2010b). Rough Set approach for categorical data clustering, International Journal of Database Theory and Application, 3(1), 33-52.

Karavidić, Z., & Projović, 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.

Law, R., &Au, N. (1998). A rough set approach to hotel expenditure decision rules induction, Journal of Hospitality and Tourism Research, 22(4), 359-375.

Law, R., & Au, N. (2000). Relationship modeling in tourism shopping: a decision rules induction approach, Tourism Management, 21(3), 241-249.

Law, R., & Au, N., (1999). A neural network model to forecast Japanese demand for travel to Hong Kong, Tourism Management, 20, 89-97.

Law, R. (2000). Back-propagation learning in improving the accuracy of neural network based Tourism demand forecasting, Tourism Management, 21, 331-340.

Lewis, C.D. (1982). International and business forecasting methods, London: Butterworths.

Li, J., Li F., & Zhou, G. (2011). Travel Demand Prediction in Tangshan City of China Based on Rough Set. Springer-Verlag Berlin Heidelberg, 440-446.

Lim, C., & McAleer, M. (2002). Time series forecasts of international travel demand for Australia,Tourism Management, 23(4), 389-396.

Liou, J. J. H., Chuang, Y. C., & Hsu, C. C. (2016). Improving airline service quality based on rough set theory and flow graphs. Journal of Industrial and Production Engineering. 33(2), 123-133.

Mazlack, L., He., A., Zhu, Y., & Coppock, S. (2000). A rough set approach in choosing partitioning attributes, Proceedings of the ISCA 13th, International Conference (CAINE-2000), 1-6.

Nguyen, L.T., Shu, H.M., Huang, F.Y., & Hsu, M.B. (2013). Accurate forecasting models in predicting the inbound tourism demand in Vietnam, Journal of Statistics and Management Systems, 16(1), 25-43.

Pamučar, D., Stević, Z., & 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.

Pankratz, A. (1983). Forecasting with univariate Box-Jenkins models: concepts and cases, New York: Wiley.

Parmar, D., Wu, T., & Blackhurst, J. (2007). MMR: an algorithm for clustering categorical data using rough set theory, Data and knowledge Engineering, 63, 879-893.

Pawlak, Z. (1982). Rough sets, International Journal of Computer and Information Science, 11, 341-356.

Pawlak, Z. (1991). Rough sets, A Theoretical Aspect of Reasoning about data, Kluwer Academic Publisher, Boston.

Pawlak, Z., & Skowron, A. (2007). Rudiments of rough sets, Information Sciences, 177(1), 3-27.

Roy, J., Adhikary, K., Kar, S., & Pamucar, D. (2018). A rough strength relational DEMATEL model for analysing the key success factors of hospital service quality. Decision Making: Applications in Management and Engineering, 1(1), 121-142.

Sharma, H. K., Kumari, K., & Kar, S. (2019). Short-term Forecasting of Air Passengers based on Hybrid Rough Set and Double Exponential Smoothing Models, Intelligent Automation and Soft Computing, 25(1), 1-13.

Sharma, H., Roy, J., Kar, S., & Prentkovskis, O. (2018). Multi-Criteria Evaluation Framework for Prioritizing Indian Railway Stations Using Modified Rough AHP-Mabac Method. Transport and Telecommunication Journal, 19(2), 113-127.

Tseng, F.M., Yu, H.C., & Tzeng, G. H. (2001). Applied hybrid Grey model to forecast seasonal time series, Technological Forecasting and Social Change, 67(2), 291-302.

Vasiljević, M., Fazlollahtabar, H., Stević, Ž., & Vesković, S. (2018). A rough multicriteria approach for evaluation of the supplier criteria in automotive industry. Decision Making: Applications in Management and Engineering, 1(1), 82-96.

Wang, C.H. (2004). Predicting tourism demand using fuzzy time series and hybrid Grey Theory, Tourism Management, 25(3), 367-374.

Winters, P.R. (1960). Forecasting sales by exponentially weighted moving averages, Management Science, 6(3), 324-342.