The authors develop a novel forecast combination approach based on the order statistics of individual predictability from panel data forecasts. To this end, define notion depth, which provides ranking among different forecasts their normalized errors during training period. is in form depth-weighted trimmed mean. derive limiting distribution combination, can readily construct prediction interva...

Sometimes, the best available information about an uncertain future is a single forecast. On the other hand, stochastic-programming models need future data in the form of scenario trees. While a single forecast does not provide enough information to construct a scenario tree, a forecast combined with historical data does—but none of the standard scenario-generation methods is suited to handle t...

The gain of SVC depends upon the type of reactive power load for optimum performance. As the load and input wind power conditions are variable, the gain setting of SVC needs to be adjusted or tuned. In this paper, an ANN based approach has been used to tune the gained parameters of the SVC controller over a wide range of load characteristics. The multi-layer feedforward ANN tool with the error ...

We conduct evolutionary programming experiments to evolve artificial neural networks for forecast combination. Using stock price volatility forecast data we find evolved networks compare favorably with a naı̈ve average combination, a least squares method, and a Kernel method on out-of-sample forecasting ability—the best evolved network showed strong superiority in statistical tests of encompassi...

Prediction of future research topics by using time series analysis either statistical or machine learning has been conducted previously by several researchers. Several methods have been proposed to combine the forecasting results into single forecast. These methods use fixed combination of individual forecast to get the final forecast result. In this paper, quite different approach is employed ...

A Bayesian methodology is used to assess the information content of categorical, probabilistic forecasts of specific variables derived from a general circulation model (GCM) forecast ensemble, and to combine a ‘‘prior’’ forecast (climatological probabilities of each category) with a categorical probabilistic forecast derived from a GCM ensemble to develop posterior, or ‘‘regularized’’ categoric...

Factors in the unit investment forecast of overhead line engineering are various and complex, it is very difficult to get the satisfied forecasting effect using traditional econometric models. In view of this characteristic, this thesis puts forward a kind of combination forecast model, using the ARIMA model and RBF neural network model to seek for linear and nonlinear change rule of historical...

This paper applies receiver operating characteristics (ROC) analysis to M3 Competition, micro monthly time series for one-month-ahead forecasts. Using the partial area under the curve (PAUC) criterion as a forecast accuracy measure and paired-comparison testing via bootstrapping, we find that complex methods (AutomatANN, Flores-Pearce2, Forecast ProSmart FCS, and Theta) perform best for forecas...