Fuzzification Methods and Prediction Accuracy of Fuzzy Autocorrelation Model

The fuzzy autocorrelation model is a fuzzified autoregressive (AR) model. The aim of the fuzzy autocorrelation model is to describe the possible states of the system with high accuracy. This model uses autocorrelation similar to the Box–Jenkins model. The fuzzy autocorrelation model occasionally increases the vagueness. Although the problem can be mitigated using fuzzy confidence intervals instead of fuzzy time-series data, the unnatural estimations do not improve. Subsequently, an alternate method was used to fuzzify the time-series data and mitigate the unnatural estimation problem. This method also improved the model prediction accuracy. This paper focuses on fuzzification method, and discusses the prediction accuracy of the model and fuzzification of the time-series data. The analysis of the Nikkei stock average shows a high prediction accuracy and manageability of a fuzzy autocorrelation model. In this pape, a quartile is employed as an alternate fuzzification method. The model prediction accuracy and estimation behavior are verified through an analysis. Finally, the proposed method was found to be successful in mitigating the problems.