A rough set approach for forecasting models
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 importa...
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Regional Association for Security and crisis management
2020-03-01
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Online Access: | https://dmame.rabek.org/index.php/dmame/article/view/52 |
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doaj-e4a41dc4c87347dc92fc938075d794032021-02-11T19:56:50ZengRegional Association for Security and crisis managementDecision Making: Applications in Management and Engineering2560-60182620-01042020-03-013112110.31181/dmame2003001s52A rough set approach for forecasting modelsHaresh Kumar Sharma0Kriti Kumari1Samarjit Kar2Department of Mathematics, National Institute of Technology, Durgapur, IndiaDepartment of Mathematics, Banasthali University, Jaipur, Rajasthan, IndiaDepartment of Mathematics, National Institute of Technology, Durgapur, IndiaThis 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.https://dmame.rabek.org/index.php/dmame/article/view/52forecasting, mean absolute percent error (mape), rough set, total roughness, maximum dependency degree |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Haresh Kumar Sharma Kriti Kumari Samarjit Kar |
spellingShingle |
Haresh Kumar Sharma Kriti Kumari Samarjit Kar A rough set approach for forecasting models Decision Making: Applications in Management and Engineering forecasting, mean absolute percent error (mape), rough set, total roughness, maximum dependency degree |
author_facet |
Haresh Kumar Sharma Kriti Kumari Samarjit Kar |
author_sort |
Haresh Kumar Sharma |
title |
A rough set approach for forecasting models |
title_short |
A rough set approach for forecasting models |
title_full |
A rough set approach for forecasting models |
title_fullStr |
A rough set approach for forecasting models |
title_full_unstemmed |
A rough set approach for forecasting models |
title_sort |
rough set approach for forecasting models |
publisher |
Regional Association for Security and crisis management |
series |
Decision Making: Applications in Management and Engineering |
issn |
2560-6018 2620-0104 |
publishDate |
2020-03-01 |
description |
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. |
topic |
forecasting, mean absolute percent error (mape), rough set, total roughness, maximum dependency degree |
url |
https://dmame.rabek.org/index.php/dmame/article/view/52 |
work_keys_str_mv |
AT hareshkumarsharma aroughsetapproachforforecastingmodels AT kritikumari aroughsetapproachforforecastingmodels AT samarjitkar aroughsetapproachforforecastingmodels AT hareshkumarsharma roughsetapproachforforecastingmodels AT kritikumari roughsetapproachforforecastingmodels AT samarjitkar roughsetapproachforforecastingmodels |
_version_ |
1724274168193613824 |