Algorithmic approaches in model selection of the air passengers flows data
Algorithm is an important element in any problem solving situation.In statistical modelling strategy, the algorithm provides a step by step process in model building, model testing, choosing the ‘best’ model and even forecasting using the chosen model.Tacit knowledge has contributed to the existenc...
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| Main Authors: | , , |
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| 格式: | Conference or Workshop Item |
| 語言: | English |
| 出版: |
2015
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| 主題: | |
| 在線閱讀: | http://repo.uum.edu.my/15488/1/PID218.pdf http://repo.uum.edu.my/15488/ http://www.icoci.cms.net.my/proceedings/2015/TOC.html |
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| 總結: | Algorithm is an important element in any problem solving situation.In statistical modelling strategy, the algorithm provides a step by step process in model building, model testing, choosing the ‘best’ model and even forecasting using the chosen model.Tacit knowledge has contributed
to the existence of a huge variability in manual modelling process especially between expert and non-expert modellers.Many algorithms (automated
model selection) have been developed to bridge the gap either through single or multiple equation modelling.This study aims to evaluate the forecasting performances of several selected algorithms on air passengers flow data based on Root Mean Square Error (RMSE) and Geometric Root Mean Square Error (GRMSE).The findings show that multiple models selection performed well in one and two step-ahead forecast but was outperformed by single model in three step-ahead forecasts. |
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