Based On Information Entropy And Particle Swarm Optimization Of Fuzzy Time Series Forecasting Model

With the globalization of finance developing rapidly, the Chinese finance is also deeper and broader integrating to the world economics. To adapt the new economic environment better and quickly, the standards of accuracy in forecasting have reached higher and higher. The accurate and reasonable forecasting of the finance time series as the important data type of finance market was able to instruct the financial investors' investment and the government regulation effectively. In view of the fact that the application of the finance time series analysis is very widespread, the numerous scholars have researched to it in various aspects. However, the finance time series have some characteristics such as high frequency, multi-dimensions, misalignment and fuzziness. which increased the difficulty of study the finance time series.Recentlt, the fuzzy time series have attracted much attention in the application of finance time series analysis. All researchers in the domain of fuzzy time series have paid much attention to the existing unsolved problems, i.e., how to partition the universe of discourse and how to construct the fuzzy logic relationships effectively. This article proposed two new algorithms to solve these questions which existed in fuzzy time series based on the predecessor works. The actual content summary is as follows:(1) To construct the fuzzy logic relations effectively, this article introduces the information entropy concept to the fuzzy set to enables the fuzzy set defuzzy the date set reasonably. Through the forecasting results of Alabama university enrollment number,000001, the Dow Jones average, USD/JPY exchange rate and so on, which indicated that introduction of the information entropy have enabled the fuzzy set have better compatibility and robustness, at the same time reduced the complexity degree of computation and made the algorithm have better performability.(2) To divisie the universe of discourse effectively and objectively, this article uses the particle swarm optimization with the random searching performance to optimize position obtains, namely universe of discourse middle point. Compared to numerous forecasting models, the proposed models not only solve the problem of artificial division universe of discourse, but also provide better forecasting performance and obtain higher accuracy rates than the existing models.