# 臺灣博碩士論文加值系統

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 In this time of big data, many people often using statistical or mathematical models to analyze historical data and use data analysis to predict the changes of future. In past studies, regression analysis, neural network and logistic regression models or support vector machine, etc., are often used to analyze.But the above methods are based on numerical data to accurately predict, for the fuzzy data (semantic information, such as weather information), these methods cannot be used to analysis. Until 1965, Zadeh proposed the fuzzy theory, the linguistic variable finally can be analyzed and discussion. Currently, the fuzzy theory has been widely used in many fields, such as fuzzy time series, fuzzy regression, etc.However, there are some problems in the fuzzy time series forecasting models. Like the limits of the numbers of variables, ignoring the frequencies of fuzzy sets. It make the large predictions error and the lack explanation ability. Therefore, in order to solve the above problems, in this study, we combine the fuzzy theory and the Markov theory, using Markov matrix instead of the traditional fuzzy relation matrix, consider the frequency of transfer status. Calculate the steady-state probabilities and use correlation coefficient to combine each variable. Build a whole new predict model and enhance the prediction accuracy of the results.
 摘要 IAbstract II誌謝 IIIContents IVList of Tables VIList of Figures VIICHAPTER 1 Introduction 11.1 Background and motivation 11.2 The contribution and the structure of this study 3CHAPTER 2 Literature Review 52.1 Fuzzy set theory 52.2 Fuzzy time series 82.2.1 Basic definition of fuzzy time series 82.2.2 Forecasting model of fuzzy time series 112.3 Markov model 212.4 Additive smoothing 24CHAPTER 3 Model Development 253.1 Fuzzifying historical data 273.2 Calculate the Correlation coefficient 283.3 Combined the Markov transition matrix 293.4 Forecasting and defuzzification 333.5 Demonstration of the proposed model 34CHAPTER 4 Evaluation and analysis 414.1 Evaluation Indicators 414.2 One-factor - Enrollment data of Alabama 424.2.1 Experimental content 434.2.2 Evaluation and analysis 494.3 Multi-factor - Weather data of Alishan 51CHAPTER 5 Conclusion and Future Work 535.1 Conclusion 535.2 Management Implication 545.3 Future Work 55Reference 56Appendixes 59
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