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Submitted version of thesis to prof

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Radu C. Martin 2021-07-12 09:28:25 +02:00
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9
50_Choice_of_Hyperparameters.tex
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@ -351,7 +351,7 @@ computations. Other good choices for the combinations of lags are
\model{2}{1}{3} and \model{1}{1}{3}, which have good performance on all four
metrics, as well as being cheaper from a computational perspective. In order to
make a more informed choice for the best hyperparameters, the simulation
performance of all three combinations has been analysed.
performance of all three combinations has been analyzed.
\begin{table}[ht]
%\vspace{-8pt}
@ -403,7 +403,7 @@ the discrepancies.
\subsubsection{Conventional Gaussian Process}
The simulation performance of the three lag combinations chosen for the
classical \acrlong{gp} models has been analysed, with the results presented in
classical \acrlong{gp} models has been analyzed, with the results presented in
Figures~\ref{fig:GP_113_multistep_validation},~\ref{fig:GP_213_multistep_validation}
and~\ref{fig:GP_313_multistep_validation}. For reference, the one-step ahead
predictions for the training and test datasets are presented in
@ -449,8 +449,9 @@ this proves to be the best simulation model.
\label{fig:GP_313_multistep_validation}
\end{figure}
Lastly, \model{3}{1}{3} has a much worse simulation performance than the other
two models. This could hint at an over fitting of the model on the training data.
Lastly, \model{3}{1}{3} (cf. Figure~\ref{fig:GP_313_multistep_validation}) has a
much worse simulation performance than the other two models. This could hint at
an over fitting of the model on the training data.
\clearpage