For promoting the forecasting performance of fuzzy time-series models, this paper proposes a new model, which incorporates the concept of the Fibonacci sequence, the framework of Song and Chissom's model and the weighted method of Yu's model. Time-series models have been utilized to make reasonably accurate predictions in the areas of stock price movements, academic enrollments, weather, etc. The results show that the new model has a clear advantage of improving the forecast accuracy.įuzzy time-series based on Fibonacci sequence for stock price forecastingĬhen, Tai-Liang Cheng, Ching-Hsue Jong Teoh, Hia At last, contrast experiments on the enrollments of the University of Alabama and the Taiwan Stock Exchange Capitalization Weighted Stock Index are carried out. On these bases, forecast rules based on intuitionistic fuzzy approximate reasoning are established. ![]() In the new model, a fuzzy clustering algorithm is used to divide the universe of discourse into unequal intervals, and a more objective technique for ascertaining the membership function and nonmembership function of the intuitionistic fuzzy set is proposed. In this regard, an intuitionistic fuzzy time series forecasting model is built. Intuitionistic Fuzzy Time Series Forecasting Model Based on Intuitionistic Fuzzy Reasoningĭirectory of Open Access Journals (Sweden)įull Text Available Fuzzy sets theory cannot describe the data comprehensively, which has greatly limited the objectivity of fuzzy time series in uncertain data forecasting. The experiment results show that the proposed ATVF model achieves a significant improvement in forecasting accuracy as compared to other fuzzy-time-series forecasting models. The performance of the ATVF model is tested using both simulated and actual time series including the enrollments at the University of Alabama, Tuscaloosa, and the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX). The proposed model automatically adapts the analysis window size of fuzzy time series based on the prediction accuracy in the training phase and uses heuristic rules to generate forecasting values in the testing phase. In this paper, an adaptive time-variant fuzzy-time-series forecasting model (ATVF) is proposed to improve forecasting accuracy. These studies use fixed analysis window sizes for forecasting. Related studies mainly focus on three factors, namely, the partition of discourse, the content of forecasting rules, and the methods of defuzzification, all of which greatly influence the prediction accuracy of forecasting models. Wong, Wai-Keung Bai, Enjian Chu, Alice Wai-ChingĪ fuzzy time series has been applied to the prediction of enrollment, temperature, stock indices, and other domains. ![]() To evaluate the model, the conventional linear regression technique is applied and the predicted values obtained are compared to the…Īdaptive time-variant models for fuzzy-time-series forecasting. Fuzzy time series, an aspect of fuzzy set theory, forecasts enrollment using a first-order time-invariant model. The concept of fuzzy time series is introduced and used to forecast the enrollment of a university. Forecasting Enrollments with Fuzzy Time Series.
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