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