Yoshiyuki Yabuuchi

Fuzzy robust regression analysis

Since a fuzzy linear regression model was proposed in 1987, its possibilistic model was employed to analyze data. From viewpoints of fuzzy linear regression, data are understood to express the possibilities of a latent system. When data have error or data are very irregular, the obtained regression model has an unnaturally wide possibility range. We propose a fuzzy robust linear regression which is not influenced by data with error. The model is built as rigid a model as possible to minimize the total error between the model and the data. The robustness of the proposed model is shown using numerical examples.<<ETX>>

Fuzzy principal component analysis for fuzzy data

A fuzzy concept is employed to construct a principal component model which can deal with fuzziness, vagueness or possibility, which is called fuzzy principal component analysis for fuzzy data. The fuzzy principal component analysis analyzes the possibility of fuzzy data. The fuzzy principal component analysis for fuzzy data has three formulations according the portions which the possibilities included in fuzzy data are embodied: (1) an eigenvalue, (2) an eigenvector and (3) both eigenvalue and eigenvector. In this paper, we discuss only the first formulation that an eigenvalue is employed to deal with fuzziness of data. The principal component analysis for fuzzy data is employed in this paper to analyze the features of information technology industry. In this analysis, the financial ratio is employed as an index. We evaluate the possibility of a company activity in the information technology industry.

Fuzzy Autocorrelation Model with Confidence Intervals of Fuzzy Random Data

Economic analyses are typical methods based on time-series data or cross-section data. Economic systems are complex because they involve human behaviors and are affected by many factors. When a system includes such uncertainty, as those concerning human behaviors, a fuzzy system approach plays a pivotal role in such analysis. In this paper, we propose a fuzzy autocorrelation model with confidence intervals of fuzzy random time-series data. This confidence intervals has an essential role in dealing with fuzzy random data on our fuzzy autocorrelation model which we have presented. We analyze tick-by-tick data of stock dealing and compare two time-series models, a fuzzy autocorrelation model proposed by us, and a new fuzzy time-series model which we propose in this paper.

Formulation of Possibility Grade-Based Fuzzy Autocorrelation Model and Its Application to Forecasting

The objective of economic analysis is to interpret the past, present or future economic state by analyzing economical data. In many cases an economic analysis is pursued based on the timeseries data or is analyzed the structure of the target system by multivariate data. Time-series analysis and regression analysis are central tools to analyze economics data. Nevertheless, economic systems are a complex system resulted from human behaviors and related to many factors. When the system includes much uncertainty such as ones of human behaviors, it is better to employ methodologies of a fuzzy system in the analysis. Yabuuchi and Watada proposed a fuzzy autocorrelation model to describe the system possibility which focal timeseries system has. However, sometimes, this model has a large width this is identified with a ambiguity of a model. In this paper, we propose a fuzzy autocorrelation model by using a possibility grade to improve this problem, and the proposed model will be compared with a fuzzy autocorrelation model by a analyzed result of the tick-by-tick data of stocks.