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In this paper, a multiple prediction system using T-S fuzzy model is presented for time series forecasting. To design predictors with better performance especially for chaos or nonlinear time series, difference data were used as their input, because they reveal the statistical patterns and the regularities concealed in time series more effectively than the original data can. The proposed method consists...
One of the most important goals of time series analysis is prediction basing on the analyzed information. But it is not easy to analyze the patterns, regularities and trends of non-stationary and/or chaos time series because their major characteristics are non-linear and vague. In this paper, we propose primary and secondary tuning procedures that can enhance the accuracy for designing fuzzy prediction...
In this paper, we used data consisting of attributes containing financial performance information on failed and non-failed banks. We developed and tested several models using three induction-based machine learning techniques (C4.5, a backpropagation neural network and SX-WEB) and linear discriminant analysis. All models showed test set classification correctness under 74% when trained and tested with...
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