## 4 Tt-Vfd-Sim
### TT-VFDL-SIM for Classification
The Original Window Size in Table 7 is the length of the original data pattern of each dataset, and the Tumbling Window Size is the length of the sub-pattern generated by tumbling windowing. Latent Dimension in Table 7 is the latent vector size of LSTM-AE for the sub-pattern, and Compressed Window Size is the latent vector size of LSTM-AE for one original data pattern. To analyze the impact of similar feature spaces on TT-VFDL-SIM, three decoded datasets with different reconstruction performance were generated for each dataset. And the three decoded datasets were introduced into the experiment. The reconstruction performance of decoded data is related to the latent vector size of LSTM-AE. We adjusted the degree to which raw data were compressed by changing the dimension of the latent vector of LSTM-AE. Compression Ratio in Table 7 is the ratio of Original Window Size to Compressed Window Size. In other words, the larger the Compression Ratio, the more the raw data are compressed and then reconstructed. In Table 7, for all datasets, the smaller the Compression Ratio, the smaller the average MSE and the larger the average \(R^{2}\), and the best reconstruction performance is shown when the Compression Ratio is the smallest. In other words, the less the raw data are compressed, the more similar decoded data can be generated to the raw data. In Table 7, HOMENV and GASVOLT have the highest reconstruction performance, with an average MSE of 0.00002 and an average \(R^{2}\) of 1.00. However, even in the worst reconstruction performance, average MSE and average \(R^{2}\) are 0.00163 and 0.95, respectively. This is very high performance in terms of general regression tasks. Therefore, it can be seen that the reconstruction performance of decoded data generated by the LSTM-AE-based Data Augmentation technique applied to each \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **Original Window Size** & **Tumbling Window Size** & **Latent Dimension** & **Compressed Window Size** & **Compressed Window Size** & **Compression Ratio** & **Average** & **Average** \\ \hline \multirow{4}{*}{AIRQUAL} & \multirow{4}{*}{24} & \multirow{4}{*}{6} & 2 & 8 & 0.33 & 3.00 & 0.00081 & 0.96 \\ \cline{5-8}  & & & 3 & 12 & 0.50 & 2.00 & 0.00017 & 0.99 \\ \cline{5-8}  & & & 5 & 20 & 0.83 & 1.20 & 0.00007 & 1.00 \\ \hline \multirow{4}{*}{HOMENV} & \multirow{4}{*}{72} & \multirow{4}{*}{6} & 2 & 24 & 0.33 & 3.00 & 0.00004 & 0.99 \\ \cline{5-8}  & & & 3 & 36 & 0.50 & 2.00 & 0.00002 & 1.00 \\ \cline{5-8}  & & & 5 & 60 & 0.83 & 1.20 & 0.00002 & 1.00 \\ \hline \multirow{4}{*}{GASVOLT} & \multirow{4}{*}{70} & \multirow{4}{*}{5} & 1 & 14 & 0.20 & 5.00 & 0.00163 & 0.95 \\ \cline{5-8}  & & & 3 & 42 & 0.60 & 1.67 & 0.00004 & 1.00 \\ \cline{5-8}  & & & 4 & 56 & 0.80 & 1.25 & 0.00002 & 1.00 \\ \hline \multirow{4}{*}{ACCHAR} & \multirow{4}{*}{204} & \multirow{4}{*}{6} & 2 & 68 & 0.33 & 3.00 & 0.00149 & 0.90 \\ \cline{5-8}  & & & 3 & 102 & 0.50 & 2.00 & 0.00007 & 1.00 \\ \cline{5-8}  & & & 5 & 170 & 0.83 & 1.20 & 0.00002 & 1.00 \\ \hline \multirow{4}{*}{TRAFFIC} & \multirow{4}{*}{144} & \multirow{4}{*}{6} & 2 & 48 & 0.33 & 3.00 & 0.00008 & 0.96 \\ \cline{5-8}  & & & 3 & 72 & 0.50 & 2.00 & 0.00003 & 0.99 \\ \cline{5-8}  & & & 5 & 120 & 0.83 & 1.20 & 0.00002 & 0.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance of the LSTM-AE-based Data Augmentation for each dataset by Compression Ratio. dataset is overall very excellent. Additionally, this also means that there is a baseline for similarity between similar feature spaces that can be introduced into TT-VFDL-SIM. ### Performance Evaluation between the TT-VFDL-SIM and Other Comparison Methods

In this section, we compare the performance of TT-VFDL-SIM and other comparison methods. As described in Section 5.1.5, TT-VFDL-SIM was applied to 45 experimental units distinguished into (FM Structure, Dataset, Compression Ratio). Other comparison methods were applied to 15 experimental units distinguished into (FM Structure, Dataset).