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\begin{table}[htb]
\caption{Training and validation with time based hyperparameters
sorted by NNSE accuracy. The table includes the best two two
values highlighted in the training and validation results to
showcase the the the accuracy of the validation. In the validation
we see that the best value for training is in rank four for the
validation. The number of Epochs for this experiment is 2.
26 is half of 52 and so 26 2-week intervals is a year.}
\label{tab:training-2}
\renewcommand{\arraystretch}{1.2}
\begin{center}
\begin{tabular}{|r|rl||rl|}
\hline
{\bf Rank} & \multicolumn{2}{c||}{\bfseries Training} & \multicolumn{2}{c|}{\bfseries Validation} \\
& {\bf NNSE} & {\bf Hyperparameters} & {\bf NNSE} & {\bf Hyperparameters} \\
\hline
1 & \color{red} 0.191300 & \color{red} Year Back & \color{blue} 0.195200 & \color{blue} 6M 2wk+7AVG} \\
2 & 0.192700 & \color{blue} 6M 2wk+7AVG & \color{teal} 0.201000 & \color{teal} 6 Months Back \\
3 & 0.197000 & 6M 2wk+13AVG & 0.201600 & 6M 2wk+13AVG \\
4 & \color{teal} 0.201600 & \color{teal} 6 Months Back & \color{red} 0.204500 & \color{red} Year Back \\
5 & 0.232600 & 1Y 2wk+13AVG & 0.219700 & 3 Months Back \\
6 & 0.233000 & 3 Months Back & 0.228900 & 3M 2wk+7AVG \\
7 & 0.235800 & 1Y 2wk+7AVG & 0.238200 & 1Y 2wk+13AVG \\
8 & 0.243000 & 3M 2wk+7AVG & 0.249500 & 1Y 2wk+7AVG \\
9 & 0.251600 & 1Y 2wk+26AVG & 0.264400 & 6M 2wk+26AVG \\
10 & 0.251700 & 6M 2wk+26AVG & 0.266200 & 3M 2wk+13AVG \\
11 & 0.278800 & 3M 2wk+13AVG & 0.270300 & 1Y 2wk+26AVG \\
12 & 0.302500 & 3M 2wk+26AVG & 0.295800 & 3M 2wk+26AVG \\
13 & 0.405600 & Now 2wk+7AVG & 0.379700 & Now 2wk+7AVG \\
14 & 0.429900 & Now 2wk+13AVG & 0.412700 & Now 2wk+13AVG \\
15 & 0.506800 & 2 weeks Now & 0.470100 & 2 weeks Now \\
16 & 0.521800 & Now 2wk+26AVG & 0.502300 & Now 2wk+26AVG \\
\hline
\end{tabular}
\end{center}
\end{table}
\begin{table}[htb]
\caption{Training and validation with time based hyperparameters
sorted by NNSE accuracy. The table includes the best two two
values highlighted in the training and validation results to
showcase the the the accuracy of the validation. In the validation
we see that the best value for training is in rank four for the
validation. The number of Epochs for this experiment is 30.
}
\label{tab:training-30}
\renewcommand{\arraystretch}{1.2}
\begin{center}
\begin{tabular}{|r|rl||rl|}
\hline
{\bf Rank} &
\multicolumn{2}{c||}{\bfseries Training} &
\multicolumn{2}{c|}{\bfseries Validation} \\
{\bf NNSE} &
{\bf Hyperparameters} &
{\bf NNSE} &
{\bf Hyperparameters} \\
\hline
1 & \color{red} 0.047600 & \color{red} Year Back & \color{red} 0.050500 & \color{red} Year Back \\
2 & \color{blue} 0.069500 & \color{blue} 6 Months Back & \color{blue} 0.070300 & \color{blue} 6 Months Back \\
3 & 0.082900 & 1Y 2wk+7AVG & 0.076500 & 1Y 2wk+7AVG \\
4 & 0.089700 & 3 Months Back & 0.090400 & 3 Months Back \\
5 & 0.171600 & 1Y 2wk+13AVG & 0.153600 & 1Y 2wk+13AVG \\
6 & 0.208100 & 6M 2wk+7AVG & 0.186200 & 6M 2wk+7AVG \\
7 & 0.319600 & 1Y 2wk+26AVG & 0.290100 & 1Y 2wk+26AVG \\
8 & 0.330300 & 3M 2wk+7AVG & 0.291900 & 3M 2wk+7AVG \\
9 & 0.341800 & 6M 2wk+13AVG & 0.302800 & 6M 2wk+13AVG \\
10 & 0.394600 & 3M 2wk+13AVG & 0.343400 & 3M 2wk+13AVG \\
11 & 0.418900 & 6M 2wk+26AVG & 0.374500 & 6M 2wk+26AVG \\
12 & 0.450800 & 3M 2wk+26AVG & 0.384100 & 2 weeks Now \\
13 & 0.488800 & 2 weeks Now & 0.398900 & 3M 2wk+26AVG \\
14 & 0.517900 & Now 2wk+7AVG & 0.409300 & Now 2wk+7AVG \\
15 & 0.559200 & Now 2wk+13AVG & 0.453000 & Now 2wk+13AVG \\
16 & 0.586000 & Now 2wk+26AVG & 0.484100 & Now 2wk+26AVG \\
\hline
\end{tabular}
\end{center}
\end{table}
\begin{table}[htb]
\caption{Training and validation with time based hyperparameters
sorted by NNSE accuracy. The table includes the best two two
values highlighted in the training and validation results to
showcase the the the accuracy of the validation. In the validation
we see that the best value for training is in rank four for the
validation. The number of Epochs for this experiment is 70.}
\label{tab:training-70}
\renewcommand{\arraystretch}{1.2}
\begin{center}
\begin{tabular}{|r|rl||rl|}
\hline
{\bf Rank} & \multicolumn{2}{c||}{\bfseries Training} & \multicolumn{2}{c|}{\bfseries Validation} \\
& {\bf NNSE} & {\bf Hyperparameters} & {\bf NNSE} & {\bf Hyperparameters} \\
\hline
1 & \color{red} 0.067400 & \color{red} 3 Months Back & \color{red}0.069800 & \color{red} 3 Months Back \\
2 & \color{blue} 0.073500 & \color{blue} Year Back & \color{blue} 0.071200 & \color{blue} Year Back \\
3 & 0.083100 & 1Y 2wk+7AVG & 0.084300 & 1Y 2wk+7AVG \\
4 & 0.105300 & 6 Months Back & 0.102200 & 6 Months Back \\
5 & 0.138400 & 6M 2wk+7AVG & 0.133700 & 6M 2wk+7AVG \\
6 & 0.153500 & 1Y 2wk+13AVG & 0.142800 & 1Y 2wk+13AVG \\
7 & 0.252100 & 6M 2wk+13AVG & 0.235400 & 6M 2wk+13AVG \\
8 & 0.295900 & 6M 2wk+26AVG & 0.269700 & 6M 2wk+26AVG \\
9 & 0.318800 & 1Y 2wk+26AVG & 0.291100 & 3M 2wk+7AVG \\
10 & 0.335400 & 3M 2wk+7AVG & 0.293500 & 1Y 2wk+26AVG \\
11 & 0.385200 & 3M 2wk+13AVG & 0.333000 & 3M 2wk+13AVG \\
12 & 0.421000 & 3M 2wk+26AVG & 0.344500 & 2 weeks Now \\
13 & 0.425700 & 2 weeks Now & 0.359400 & Now 2wk+7AVG \\
14 & 0.441300 & Now 2wk+7AVG & 0.370700 & 3M 2wk+26AVG \\
15 & 0.465800 & Now 2wk+13AVG & 0.385800 & Now 2wk+13AVG \\
16 & 0.490400 & Now 2wk+26AVG & 0.412500 & Now 2wk+26AVG \\
\hline
\end{tabular}
\end{center}
\end{table}