A steady-state probabilities model for fuzzy time series forecasting

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, etc., are often used to analyze. But the above methods are based on numerical data to accurately predict, for the fuzzy data, 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. For enhancing the prediction accuracy, there are still some issues in the study of fuzzy time series. 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. Build a whole new predict model and enhance the prediction accuracy of the results. Finally, to justify the effeteness of the proposed forecasting model, we compare and analyze on prediction accuracy with real-world data sets.