Sentence examples for real world forecast from inspiring English sources

The challenges for moving toward a more objective use of seasonal climate forecasts to help support decision making involve more efficient interaction among climate scientists, system scientists and decision makers, with the end user driving the skill assessment of the entire end-to-end forecasting system through real world forecast applications.

This also means that in a real world forecast climatic models needs to be developed and incorporated in detail into the biological considerations.

It is used to apply neural networks to solve real-world forecasting, classification and function approximation problems.

Many recent studies propose wavelet-based hydrological and water resources forecasting models that have been incorrectly developed and that cannot properly be used for real-world forecasting problems.

As a first approximation to real-world forecasting approaches, in the simulations reported here the provider uses the Holt-Winter exponential smoothing algorithm to forecast energy usage [15].

In this vein, we contribute a set of best practices, which focusses on deriving "boundary-corrected" wavelet and scaling coefficients from time series data, overcoming the boundary condition issues and providing hydrological and water resources modellers with a justified and coherent strategy for developing wavelet-based forecasting models that may be used for real-world forecasting problems.

To simulate the operation of real world, a forecast example is given below, with the hypothesis that dispatched power is derived from hourly average of forecasting power.

Examples like these of predictions failing badly in the real world are very common, and forecasts that seem too good to be true usually are.

This model enables complex data to form information granules with semantic descriptions and mine complex data in the new feature space, which is in line with human thinking and background knowledge of air quality forecasting in the real world.

This is not the case when dice are loaded or in such real world cases as radioactive decay or weather forecasting.

Much of the RED project supports "exposure forecasting" or "ExpoCast" research to provide real world context for ToxCast data.