Abstract
Fuzzy Sets Theory was introduced by L. A. Zadeh in 1965. Up to now, fuzzy sets have been applied to many fields such as Decision Analysis, System Theory, Artificial Intelligence, Economics and Control Theory. However, until 1993, Q. Song and B.S Chissom proposed a fuzzy time series method which provides an alternative approach for some special dynamic process.This paper presents two methods to forecast secular trend and seasonal variation time series problems respectively. The revised fuzzy time series method uses Song and Chissom’s first-order time-invariant model to predict such linguistic historical data problems and we illustrate the forecasting process by the enrollments of the University of Alabama. This method obtains a better average error than the error in Song and Chissom’s method. The method using fuzzy regression theory solves the shortcoming that fuzzy time series method could not work in dealing with seasonal variation time series problems. Under different confidence level the resultant forecasting interval would provide more flexibility for a decision maker in making decisions.ABSTRACT iiACKNOWLEDGEMENTS iiiCONTENTS ivTABLE CAPTIONS ivFIGURE CAPTIONS ivLIST OF NOTATIONS ivChapter 1 INTRODUCTION 1Chapter 2 LITERATURE REVIEW 32.1 Fuzzy Time Series 32.2 Fuzzy Regression 82.3 Conclusions 11Chapter 3 A FUZZY TIME SERIES MODEL FOR SECULAR DATA 123.1 S&C Fuzzy Time Series Method 133.2 A Revised Fuzzy Time Series Method 203.3 Evaluation and Discussion 25Chapter 4 FUZZY REGRESSION METHOD FOR SEASONAL TREND 304.1 Fuzzy Regression Model 314.2 Conclusion and Discussion 36Chapter 5 SUMMARY AND CONCLUSION 38REFERENCE 40