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Before Regulation FD, the study found, 30percentt of analyst forecasts for a given company came on multiforecast days.
This approach would allow the identification of methods that provide the most accurate short-term, intermediate-term and long-term forecasts, for a given setting and a set of predictors.
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High-resolution forecasting already requires more processing power to provide a forecast for a given area within a given time: halving the grid spacing (from 12km to 6km, for example) increases the amount of computing power needed fourfold.
In a 1965 paper [ 14], Lorenz suggested that, instead of running one forecast from a best guess of the initial state, one should run an ensemble of many forecasts, each from a statistically equivalent estimate of the initial state, to give a Monte Carlo estimate of the forecast uncertainty for a given weather model.
This is valuable because it allows us to make forecasts for any given technology with a clear understanding of the quality of the forecasts.
Aggregated together, the service provides a statistically more accurate forecast for any given location.
Engineers have the technical means to forecast the well productivity for a given horizontal length.
The dynamic models that include wave-driven water levels forecast much greater impact for a given water level, or sooner in the future, than passive models.
Accordingly, the fields to be selected as the targets will affect the final forecast for any given target topic.
Once a satisfactory model has been obtained, it can be used to forecast expected numbers of cases for a given number of future time intervals [ 24].
Initial results (Prikryl et al. 2012) have demonstrated the feasibility of probabilistic forecasting of scintillation in the cusp for a given location of a GPS receiver.
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