A rough set approach for forecasting models

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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Published

2020-03-14

How to Cite

Sharma, H. K., Kumari, K., & Kar, S. (2020). A rough set approach for forecasting models. Decision Making: Applications in Management and Engineering, 3(1), 1–21. https://doi.org/10.31181/dmame2003001s

Issue

Vol. 3 No. 1 (2020): Decision Making: Applications in Management and Engineering

Section

Regular articles

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