Applying Fuzzy C-Means and Artificial Neural Networks into a High-Order Fuzzy Time Series Prediction Model
نویسنده
چکیده
A novel high-order fuzzy time series model for stock price forecasting is presented based on the fuzzy cmeans (FCM) discretization method and artificial neural networks (ANN). In the proposed model, the FCM discretization method obtained reliable interval lengths. In addition, the fuzzy relation matrix was obtained from ANN, mooting the need for complex and time-consuming matrix operations. The proposed model was validated using experimental datasets from authentic university enrollment data. Empirical results indicate the proposed model outperforms existing methods for forecasting time series in terms of root mean squared errors (RMSE).
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