Enhanced Taguchi’s T-method using angle modulated Bat algorithm for prediction

Abstract

Analysis of multivariate historical information in predicting future state or unknown outcomes is the core function of Taguchi’s T-method. Introduced by Dr. Genichi Taguchi under Mahalanobis-Taguchi system, the T-method combines regression principle and robust quality engineering element in formulating a predictive model and employs taguchi’s orthogonal array design in optimizing the model through feature or variable selection process. There is a concern regarding the sub-optimality of the T-method prediction accuracy, particularly when the orthogonal array failed to offer a significant number of combinations in search for an optimal subset of features. This is due to the fixed and limited combination offered for evaluation as well as the lack of higher-order interaction of combination. In response to this issue, this paper proposed an angle modulated Bat algorithm to be integrated with the T-method in optimizing the prediction model. A comparison study was conducted using energy efficiency benchmark datasets with the mean absolute error metric used as the performance measure. The results show that the proposed method improved the prediction accuracy by 10.74%, from 6.05 to 5.4, by integrating only four features over the original eight in the prediction model.

Application of Mahalanobis-Taguchi System in Rainfall Trends at UMP Gambang Campus

“…This research uses MTS to classify and optimize the parameters collected to find the significant parameters. MTS is a powerful methodology that combines the Mahalanobis distance measure with Taguchi's orthogonal array design for analyzing multivariate data and making quantitative decisions [6]. It has been widely applied in various fields, including engineering, manufacturing, medicine, and more [7].…”

Application of Mahalanobis-Taguchi system in Rainfall Distribution

“…The objective of this research is to classify and optimize the gathered data in order to locate the parameters that are significant by using Mahalanobis-Taguchi system (MTS). MTS is a strong approach for multivariate data analysis and quantitative decision-making that uses Mahalanobis distance (MD) measure and Taguchi's orthogonal array (OA) design [6]. MTS can handle non-linear patterns and analyse correlations between independent variables or components, making ABSTRACT -The rainfall time series is often nonlinear and multi-time scale because of hydrology, meteorological, and human activity.…”