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Out-of-sample results indicate that these rules are effective.
Fig. 4 Comparison of out-of-sample forecasts.
Thus, they can be used for out-of-sample validation.
However, their out-of sample period was only 2 month.
Therefore, all predictions are true out-of-sample predictions.
There is no out-of-sample information on other DMUs.
This is confirmed by a (partially) out-of-sample test.
The models estimated are utilized for out-of-sample forecasting.
So we are considering the option 1) and 2) with the evaluation for all out-of-sample days and all non-holidays in the out-of-sample period.
In-the-sample and out-of-sample forecasting further support our empirical evidence.
We compute an operational VaR and we conduct out-of-sample backtesting.
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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