Sentence examples for efficiently forecasting from inspiring English sources

One learns, with considerable admiration, that nearly any member of the phylum and there are more than a hundred thousand species living in saltwater and fresh, tropical and temperate climes can, in theory, produce a pearl, its shell efficiently forecasting the gem's color.

One learns, with considerable admiration, that nearly any member of the phylum — and there are more than a hundred thousand species living in saltwater and fresh, tropical and temperate climes — can, in theory, produce a pearl, its shell efficiently forecasting the gem's color.

With this data, FarmLogs' platform can help crop farmers quickly and efficiently forecast profits, track expenses and more efficiently schedule operations.

FarmLogs' data-driven approach to farming leans on the mobile web to help crop farmers quickly and efficiently forecast profits, track expenses and more efficiently schedule operations.

FarmLogs' data-driven approach to farming leans on the mobile web to help crop farmers quickly and efficiently forecast profits, track expenses and more efficiently schedule operations.

Yieldex, not to be confused with similar service YieldBuild, has a solution for managing ad inventory, enabling Web publishers to allocate advertising campaigns more efficiently by forecasting overlapping inventory and predicting how ads are going to deliver.

With the increase of flexibility and production rates, the complexity of manufacturing systems reached a point where the operator in charge of the production activity control of the system is not able to forecast efficiently the impact of his decisions on the global performances.

Shifting to Intel, they similarly reported record net income and upped margin guidance for the year as factories output new generation 32nm microprocessors more efficiently than forecast.

The ANN efficiently was able to forecast the simultaneous BG and EB removal that was confirmed by reasonable numerical value i.e. MSE of 0.0021 and R2 of 0.9589 and MSE of 0.0022 and R2 of 0.9455 for testing data set, respectively.

However, forecasting the effect of such programs on power demand requires accurate models that can efficiently describe and predict changes in consumer activities as a response to pricing alterations.

Simulation results demonstrate that the proposed method can approximate the nonlinear function and forecast the time series efficiently.