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This paper presents a comparison of data mining techniques for wind power forecasting in a time frame out to 15 minutes ahead. The forecasting is focused on the power generated by the wind farms and the power changes are predicted by using multivariate time series models ARMA, focus time-delay neural network (FTDNN) and a phenomenological model of the turbines. All these models are tested with real...
A novel time series analysis is presented to analyze and forecast nonlinear random vibration signals. Mathematical models are established to describe vibration signals. First, the non-stationary vibration signals acquired in the field are transformed to stationary time series. Second, the time series models are constructed from the selected reference signals, and nonlinear least square method is used...
In this study, we propose a business intelligent model integrating econometric models, i.e. ARMA, GARCH, and ANN models for VaR estimation. The business intelligent model achieves better efficiency in input variables selecting because they are selected and newly created by time series models. Repetitive trial error process could be effectively eliminated to one time series process. On the other hand,...
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