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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].
The Every Earthquake a Precursor According to Scale (EEPAS) model (Rhoades and Evison, 2004) is an earthquake forecasting method based on the precursory scale increasephenomenon (Evison and Rhoades, 2001 , 2002 2004) and associated predictive scaling relations linear regressions of mainshock magnitude, logarithm of precursor time and logarithm of precursor area on precursor magnitude (Fig. 1).
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Furthermore, the authors in [15] propose a self-adaptive method that uses a decision tree to assign the incoming workload to one of the forecasting methods based on the workload characteristics.
We imputed values for mean daily wheeze on 121 (7.6% of 1,583) days for which no wheeze data were available with forecasting methods based on the ARIMA procedure.
In [19], a signal strength forecast method based on the classification and regression trees is proposed as another application of MDT.
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.
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.
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