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In [19], a signal strength forecast method based on the classification and regression trees is proposed as another application of MDT.
(2015) proposed a tsunami forecast method based on a data assimilation technique using a dense offshore observation network rather than using seismic source parameters or the initial height of the sea surface.
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A forecasting method based on TFRWν-SVRM and PSO are put forward.
In this paper, a particular time series forecasting method based on Kohonen maps is described.
In this work, a novel multi-step ahead forecasting method based on multi-kernel learning is developed.
It starts from profiles of renewable generation and load demand, which are obtained by a forecasting method based on suitably chosen and trained Artificial Neural Networks.
A Wind power forecasting method based on the use of discrete time Markov chain models is developed starting from real wind power time series data.
A probabilistic load forecasting method based on the statistics characteristic of load forecasting error is proposed for the field of short-term load forecasting [9].
Wang and Xiong [22] develop a hybrid forecasting method based on an ARMA process, outlier detection, and fuzzy time series to forecast the daily wind speed in Taiwan.
A novel short term wind power forecasting method based on outlier smooth transition autoregressive (OSTAR) structure is advanced, then, combined with the generalized autoregressive conditional heteroskedasticity (GARCH) model, the OSTAR-GARCH model is proposed for wind power forecasting.
Wind power forecasting method based on empirical mode decomposition (EMD) and support vector machine (SVM) was proposed to cope with the nonlinearity and non-stationarity of wind speed data.
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