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The phrase "out of sample" is correct and commonly used in written English, particularly in statistics and data analysis contexts. It refers to data or observations that are not included in a particular sample or dataset, but are still relevant to the topic being studied. Example: "In order to properly evaluate the accuracy of our model, we must test it on out of sample data." The phrase is used here to indicate that the model will be tested on data that was not used to create or train the model, providing a more accurate assessment of its performance.
Exact(1)
This "out of sample" process was repeated 10 times and cross-validated R values were computed.
Similar(59)
However, their out-of sample period was only 2 month.
This is not rigorous enough.One way round this problem is to use "out-of-sample" testing.
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.
The models estimated are utilized for out-of-sample forecasting.
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.
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.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com