## 5 Results
### Evaluation Metrics Results
The WaveNet model is an optimized version of the Conv-1D CNN model that uses atrous convolution to expand the perceptual field with a low number of parameters, but in the case of the journal metrics time series dataset, containing only three timesteps per input, the Conv-1D CNN model is able to obtain a more comprehensive perceptual field and features as well, although the network structure is shallower and has more parameters. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Methods** & **MSE** & **MAE** & **RMSE** & **MAPE** & **NRMSE** & **80\%** & **85\%** & **90\%** \\ \hline \multirow{4}{*}{\begin{tabular}{c} Machine \\ Learning \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} Multiple \\ Linear Regression \\ \end{tabular} } & \multirow{2}{*}{0.00044} & \multirow{2}{*}{0.01393} & \multirow{2}{*}{0.02109} & \multirow{2}{*}{22.143\%} & \multirow{2}{*}{0.02516} & \multirow{2}{*}{68.433\%} & \multirow{2}{*}{55.899\%} & \multirow{2}{*}{40.645\%} \\ \cline{2-2} \cline{6-8}  & & & & & & & & \\ \cline{2-2} \cline{6-8}  & \multirow{2}{*}{\begin{tabular}{c} Random \\ Forest \\ \end{tabular} } & \multirow{2}{*}{0.00047} & \multirow{2}{*}{0.01342} & \multirow{2}{*}{0.02174} & \multirow{2}{*}{19.921\%} & \multirow{2}{*}{0.02594} & \multirow{2}{*}{71.152\%} & \multirow{2}{*}{59.447\%} & \multirow{2}{*}{43.687\%} \\ \cline{2-2} \cline{6-8}  & & & & & & & & \\ \cline{2-2} \cline{6-8}  & \multirow{2}{*}{\begin{tabular}{c} XGBoost \\ LightGBM \\ \end{tabular} } & \multirow{2}{*}{0.00058} & \multirow{2}{*}{0.01456} & \multirow{2}{*}{0.02418} & \multirow{2}{*}{21.963\%} & \multirow{2}{*}{0.02884} & \multirow{2}{*}{68.203\%} & \multirow{2}{*}{56.359\%} & \multirow{2}{*}{39.862\%} \\ \cline{2-2} \cline{6-8}  & & & & & & & & \\ \hline \multirow{4}{*}{\begin{tabular}{c} Deep \\ Learning \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} ANN \\ CNN \\ \end{tabular} } & \multirow{2}{*}{0.00047} & \multirow{2}{*}{0.01388} & \multirow{2}{*}{0.02165} & \multirow{2}{*}{23.472\%} & \multirow{2}{*}{0.02583} & \multirow{2}{*}{68.525\%} & \multirow{2}{*}{57.281\%} & \multirow{2}{*}{42.074\%} \\ \cline{2-2} \cline{6-8}  & \multirow{2}{*}{\begin{tabular}{c} Conv-1D \\ CNN \\ \end{tabular} } & \multirow{2}{*}{0.00047} & \multirow{2}{*}{0.01369} & \multirow{2}{*}{0.02169} & \multirow{2}{*}{21.560\%} & \multirow{2}{*}{0.02588} & \multirow{2}{*}{69.032\%} & \multirow{2}{*}{58.479\%} & \multirow{2}{*}{42.258\%} \\ \cline{2-2} \cline{6-8}  & \multirow{2}{*}{\begin{tabular}{c} WaveNet \\ \end{tabular} } & \multirow{2}{*}{0.00050} & \multirow{2}{*}{0.01401} & \multirow{2}{*}{0.02225} & \multirow{2}{*}{22.983\%} & \multirow{2}{*}{0.02655} & \multirow{2}{*}{67.604\%} & \multirow{2}{*}{56.866\%} & \multirow{2}{*}{41.567\%} \\ \cline{2-2} \cline{6-8}  & \multirow{2}{*}{\
\begin{tabular}{c} LSTM \\ GRU \\ \end{tabular} } & \multirow{2}{*}{**0.00037**} & \multirow{2}{*}{**0.01238**} & \multirow{2}{*}{**0.01914**} & \multirow{2}{*}{**19.704\%**} & \multirow{2}{*}{**0.02283**} & \multirow{2}{*}{**72.442\%**} & \multirow{2}{*}{**61.521\%**} & \multirow{2}{*}{**46.083\%**} \\ \cline{2-2} \cline{6-8}  & \multirow{2}{*}{
\begin{tabular}{c} GRU \\ \end{tabular} } & \multirow{2}{*}{0.00046} & \multirow{2}{*}{0.01304} & \multirow{2}{*}{0.02154} & \multirow{2}{*}{19.972\%} & \multirow{2}{*}{0.02570} & \multirow{2}{*}{71.521\%} & \multirow{2}{*}{60.046\%} & \multirow{2}{*}{43.548\%} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluation metric record table. The best data in each column are displayed in bold. Therefore, the WaveNet model has a worse combined effect than the Conv-1D CNN model and is the worst-performing deep learning method. Overall, for the dynamic journal evaluation task and the journal metrics time series dataset, the models built by machine learning perform better than the models built by multiple linear regression and random forest.