Sentence examples for precision in forecasting from inspiring English sources

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Therefore, taking antimicrobial drug use into account helped to improve the precision in forecasting the monthly %MRSA by 64%, which is a clear indication that antimicrobial drug use has a substantial causal effect on the %MRSA.

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And if employers can't measure these figures easily, will resources be allocated efficiently?I am not trying to suggest that economists don't ponder these questions but I am trying to say that the measurement uncertainties make any attempt at precision in forecasts a waste of time.

On the other hand, treating travel behaviour within the framework of discrete choice models may not be that effective because, with the spatial and temporal dimensions, the choice set becomes astronomically large if one wishes to gain adequate levels of precision in forecasts.

The results indicated that the PCA_SOFM model had more ability, precision and flexibility in forecasting the pull-off adhesion between concrete layers parameter than the two mentioned models.

To verify the forecast precision, the load forecasting method is firstly adopted to obtain the baseline load values of one public building's CAC system in Nanjing on July 30th , 2013

Our results demonstrate statistically significant improved prediction accuracy for 3, 5, and 7 day census forecasts and increased precision of our forecasting model compared to a forecasting approach that ignores patient-specific information.

In this paper, in order to improve the precision of forecasting, we proceed a further optimization on training data and construct the similarity score set.

However, a fact could not be neglected that most of such existing ANN-based models have not yet satisfied researchers and engineers in forecast precision so far, and the generalization capability of these networks needs further improving.

Although the Bootstrap Feature Subsets Ensemble (BFSE) temperature model is prominent in the precision and the forecasting speed on the large-scale and noisy data, its main drawback is too many sub-models required to combine, which is not always feasible for applications.

Take IBM's Deep Thunder system, which provides precision weather forecasting to farmers via their mobile phones.

Greater precision in describing plantations should lead to more accurate forecasts and therefore better commercial decisions.

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