抄録
Taguchi's T-method is a method used to predict an output value from multivariate data. The T-method has the advantage that calculation is simpler than multiple regression analysis. In time series analysis, it is important to reduce the amount of calculation. In this paper, we propose a method of applying the T-method to a univariate time series analysis. Moreover, we propose simulating this under various conditions. For prediction accuracy, we compare the T-method, the fit of the AR model, and improvement T-methods proposed in the previous research. Improvement T-methods are Ta-method and Tb-method. This study shows that prediction using T-methods is more accurate than prediction using an AR model under certain conditions. When time series data to be analyzed is close to a nonstationary model, the accuracy of prediction using the AR model worsens. This is because the AR model assumes stationarity but, Taguchi's T-method does not. As a result, we can show that using a T-method for univariate time series analysis is robust against the target model and autoregressive coefficient value. That is, in this method, there is little decrease in prediction accuracy owing to the difference in the nature of time series data.
