is a relevant choice because two approaches are tested on the same trajectory dataset. As usual, we reject the null hypothesis if the p-value is lower than 0.05.

Methodology {#methodology}
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The most naive way to learn the parameters of the model from the observed altitudes is to directly apply Eq.\[eq:opt\] from subsection \[subsec:method\] and to apply them to generate the rest of the trajectory. By doing so, the default model is always better than the fitted one with a significant p-value. The reason behind this result is simply that the fitted model does not generalize over all the behaviors of the aircraft. In other words, it is fitted only to the behavior captured in the observations. This problem is referred to overfitting in Machine Learning litterature. To circumvent this problem, we must think of the trajectory as a time serie where the observations arrive with an determined order, i.e. the temporal order. So, at first, we will always observe the initial climb where we know from Table \[tab:badaVad\] that the inaccuracy of the model is the greatest. Furthermore, we are more interested by the parameter values that fit better the positions near the present time than at the beginning 