An overview of Taguchi’ s T-Method as a prediction tool for multivariate analysis
Analysis of prediction has attracted considerable interest in various fields. Taguchi’s T-Method is a prediction method introduced by Genichi Taguchi in mid-year 2000, among several other Mahalanobis Taguchi system tools. It was explicitly created for the prediction of multivariate data. Taguchi...
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my.utm.883322020-12-15T10:53:10Z http://eprints.utm.my/id/eprint/88332/ An overview of Taguchi’ s T-Method as a prediction tool for multivariate analysis N. Harudin, Jamaludin K.R. Ramlie, Faizir M. Nabil, Muhtazaruddin Z.M., Marlan W.Z.A.W., Muhamad N.N., Jaafar TA Engineering (General). Civil engineering (General) Analysis of prediction has attracted considerable interest in various fields. Taguchi’s T-Method is a prediction method introduced by Genichi Taguchi in mid-year 2000, among several other Mahalanobis Taguchi system tools. It was explicitly created for the prediction of multivariate data. Taguchi's T-Method has shown that even with limited sample size, making a prediction based on historical data is possible. The key elements that have been adapted in reinforcing Taguchi’s TMethod robustness are by introducing the unit-space principle and adaptation of the signal to the noise ratio (SNR) as a weighting as well as a zero-proportional theory, as proposed by Genichi Taguchi in a robust model. Taguchi’s T-Method was widely practicing in Japan and began to be practiced by non-Japanese researchers due to its simplicity and simple understanding. Up to recent, various applications of Taguchi’s T-Method been applied, which prove to be beneficial to industrial needs. This research paper outlines the T-method procedures by applying it in a few benchmark datasets and compare the accuracy with the existing multiple linear regression method for an overview. The results show that Taguchi’s T-Method is better than multiple regression in dealing with limited sample data in which the sample size is smaller than the input variables. Taguchi’s TMethod proved to have the ability to predict output with an acceptable range of prediction accuracy. Universiti Teknologi Malaysia 2019 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/88332/1/RamlieFaizir2019_AnOverviewofTaguchi%E2%80%99ST-MethodasAPrediction.pdf N. Harudin, Jamaludin K.R. and Ramlie, Faizir and M. Nabil, Muhtazaruddin and Z.M., Marlan and W.Z.A.W., Muhamad and N.N., Jaafar (2019) An overview of Taguchi’ s T-Method as a prediction tool for multivariate analysis. Open International Journal of Informatics, 7 (1). pp. 158-166. ISSN 2289-2370 http://apps.razak.utm.my/ojs/index.php/oiji/article/view/207/161 |
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TA Engineering (General). Civil engineering (General) N. Harudin, Jamaludin K.R. Ramlie, Faizir M. Nabil, Muhtazaruddin Z.M., Marlan W.Z.A.W., Muhamad N.N., Jaafar An overview of Taguchi’ s T-Method as a prediction tool for multivariate analysis |
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Analysis of prediction has attracted considerable interest in various fields. Taguchi’s T-Method is a prediction method introduced by Genichi Taguchi in mid-year 2000, among several other Mahalanobis Taguchi system tools. It was explicitly created for the prediction of multivariate data. Taguchi's T-Method has shown that even with limited sample size, making a prediction based on historical data is possible. The key elements that have been adapted in reinforcing Taguchi’s TMethod robustness are by introducing the unit-space principle and adaptation of the signal to the noise ratio (SNR) as a weighting as well as a zero-proportional theory, as proposed by Genichi Taguchi in a robust model. Taguchi’s T-Method was widely practicing in Japan and began to be practiced by non-Japanese researchers due to its simplicity and simple understanding. Up to recent, various applications of Taguchi’s T-Method been applied, which prove to be beneficial to industrial needs. This research paper outlines the T-method procedures by applying it in a few benchmark datasets and compare the accuracy with the existing multiple linear regression method for an overview. The results show that Taguchi’s T-Method is better than multiple regression in dealing with limited sample data in which the sample size is smaller than the input variables. Taguchi’s TMethod proved to have the ability to predict output with an acceptable range of prediction accuracy. |
format |
Article |
author |
N. Harudin, Jamaludin K.R. Ramlie, Faizir M. Nabil, Muhtazaruddin Z.M., Marlan W.Z.A.W., Muhamad N.N., Jaafar |
author_facet |
N. Harudin, Jamaludin K.R. Ramlie, Faizir M. Nabil, Muhtazaruddin Z.M., Marlan W.Z.A.W., Muhamad N.N., Jaafar |
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N. Harudin, Jamaludin K.R. |
title |
An overview of Taguchi’ s T-Method as a prediction tool for multivariate analysis |
title_short |
An overview of Taguchi’ s T-Method as a prediction tool for multivariate analysis |
title_full |
An overview of Taguchi’ s T-Method as a prediction tool for multivariate analysis |
title_fullStr |
An overview of Taguchi’ s T-Method as a prediction tool for multivariate analysis |
title_full_unstemmed |
An overview of Taguchi’ s T-Method as a prediction tool for multivariate analysis |
title_sort |
overview of taguchi’ s t-method as a prediction tool for multivariate analysis |
publisher |
Universiti Teknologi Malaysia |
publishDate |
2019 |
url |
http://eprints.utm.my/id/eprint/88332/1/RamlieFaizir2019_AnOverviewofTaguchi%E2%80%99ST-MethodasAPrediction.pdf http://eprints.utm.my/id/eprint/88332/ http://apps.razak.utm.my/ojs/index.php/oiji/article/view/207/161 |
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1687393557624651776 |
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13.18916 |