An Overview of Taguchi’ S T-Method as A Prediction Tool for Multivariate Analysis

Abstract

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 T- Method 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 T- Method proved to have the ability to predict output with an acceptable range of prediction accuracy.