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However, the uncertainty about the predictions was much better quantified by the non-stationary variance model, as indicated by the mean and median of the standardized squared prediction error and by accuracy plots.
According to our sensitivity analysis, the uncertainty of our predictions was much lower with S4, S5 and S6 than with other strategies (see figure 4).
It might have been reasonable a few years ago, when the quality of structure predictions was much poorer, and rewarding for the spatial closeness of structure regions to the target would suffice to discriminate between models.
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The LLSM predictions were much more accurate than the SLSM.
The ANN predictions were much better than those (±24.2%) obtained by the second-order polynomial equation from the RSM.
Therefore, the method presented here both produces new predictions for more ADRs, and the validation rate of new predictions is much better.
The messy map is a challenge for political analysts, who are finding predictions are much harder than they used to be.
This paper focuses on the climb phase, because predictions are much less accurate in this phase than in the cruising phase.
When more than one parameter value needed to be estimated, predictions were much poorer, mainly due to the large uncertainty associated with the parameter value estimates.
Compared with the approximate growth models that have been reported, e.g., the invariant field model, the SFFK model and the model by Chen et al., the proposed model's predictions are much closer to the direct detailed approach, especially for growth under high supersaturation conditions.
It shows that while the original numerical estimation has low R and high RMSE and MAE (more so for the V- component) the revised predictions were much satisfactory up to 5 days in future.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com