observed positions and the modeled one. As we will see, the problem of overfitting, which is studied by statistics and machine learning, arises in our context. The current section presents the difficulties and the solutions chosen with the associated results.

Design of Experiment
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In order to verify that the idea of an online predictor, as the one mentioned previously, is valid, we must do an empirical evaluation of the chosen algorithm by replicating the same context. The main hypothesis is that from the observation set, we can determine the values of parameters that will be fitted to the current flight. So the trajectory is separated in two subsets: the observed altitudes from the beginning to the present and the future altitudes from the present to the top of climb. As in the subsection \[subsec:result\], we use metering points to evaluate the quality of the prediction by computing the errors between the predicted altitudes and the real ones. To distinguish if a set of errors is statistically greater than the other one, we use a Wilcoxon signed-rank test. The null hypothesis of this test is that two related paired samples come from the same distribution. In our case, this test 