Wen-Gang Che

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The forecasting problem of time series is an intriguing and pivotal research topic. Due to salient capabilities of tracking uncertainty and vagueness in observations, fuzzy time series has received more and more attention from not only researchers but investors. However, there exist two unsolved problems in the modeling of fuzzy time series, i.e., how to(More)
How to use the incremental training corpus to improve the question classification accuracy rate in the process of question classification based on statistic learning. A question classification method based on the incremental modified Bayes was presented in this paper. The method used the modified Bayes and combined the incremental learning to correct the(More)
Use the tree data structure in the Elliott Wave Theory, and establish the identification system of the Elliott Wave. Run the system and find the typical Elliott Wave cycle structures randomly. Collect the corresponding period of the Elliott Waves and study the period with the K-means cluster. The results show that the Eliot wave typical loop structure(More)
The financial data are usually highly noisy and contain outliers, while detecting outliers is important but hard problem. On the other hand, efficient markets hypothesis demonstrates that market prices fully reflect all available information. Furthermore, previous studies suggest that public information arrivals could lead to volatility of stock prices.(More)
Fuzzy time series forecasting model is an effective method to solve the nonlinear problems forecasting. However, most published fuzzy time series based models did not count the change trend implicit in historical datum. In this paper, authors proposed a novel method which applied heuristic information to the fuzzy time series model based on Fibonacci(More)
The paper analyzes technology specific morphological characteristics of securities time series, proposes data structure, and takes the turning point of the short-term trend as the observation point to identify specific technical form to obtain its case data set. By Q cluster analysis based on stocks of nine financial indicators, we build a case library to(More)
With regard to the strong fluctuation of the time series of stock, due to the characteristics of the Gompertz growth curve, it has greater limitations that direct application of the Gompertz growth curve to forecast for the time series of stock. This paper takes the Composite Index of Shanghai Stock Exchange as the object of study, tries to find the local(More)
The financial market is the important component part of civil economy, especially dominant securities business. So how to analyze exactly influence factor of financial market in all lines that is concerned universally by researchers. In order to further quantify key factor of securities business wave, my article combines with fuzzy c-means (FCM), aiming at(More)
Fuzzy time series (FTS) has become an effective method for forecasting some typical time series-enrollments, stock price and daily price of foreign exchange-due to its salient capacities for dealing with uncertainty, vagueness in historical observations. However, how to partition the universe of discourse and how to construct fuzzy logic relationships are(More)
Financial forecasting has become an important and challenging task for both researchers and investors. In order to improve the forecasting accuracy rate, in this paper, a modified heuristic model of fuzzy time series using FCM is proposed. Using the daily prices of USD/JPY and USD/CHY exchange rates as testing data, the empirical results show that the(More)