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Of course the latter is a forecast.
Ninety degrees and 50-plus percent humidity is a forecast for a fashion disaster.
It is a forecast for wheat production in Europe, and it has limitations.
The only potential obstacle is a forecast for Thursday that calls for rain all day.
The result is a forecast of slower growth and rising unemployment as austerity begins to bite.
This is only loose-form guidance because it is a forecast, not a commitment.Then there is strict-form guidance – or to use another term, 'pre-commitments'.
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Our design is a forecasting task with costly information and a clear cost benefit structure.
To evaluate the goodness-of-the-fit the AIC criterion could have been used, but instead we have preferred to work with the Cross-Validation (CV) because it is a forecast-based criterion.
First, although the AIC criterion could be used, we have preferred to work with Cross-Validation (CV) which is a forecast-based criterion defined as: C V = 1 n ∑ i = 1 n (y i - ŷ - 1 ) 2 where y i denotes the i - th observation of the response variable and ŷ - i denotes the value predicted by the model, fitted by using all the observed values except the i - th observation.
Here's a forecast: the bad news is just beginning.
It's in one day and it's outside, and there's a forecast for rain.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com