## 5 Experiments
### Performance Comparison  
We compare our approach with the traditional moving average indicators, i.e., SMA, EMA, and WMA, as well as the modern sequential modeling methods including LSTM and transformer, in terms of time series prediction. Table 4 lists the performance comparison of different methods. From the results, we can see that:  
* In most cases, traditional moving average indicators fall considerably behind the deep model-based methods. This highlights the merit of deep learning techniques for time series prediction.
* Among deep sequence models, transformer and our approach are both superior to TCN, DA-RNN, and LSTM. This underlines the benefit of introducing the self-attention mechanism to capture the dynamics of time series. On the other hand, our approach obtains the best results on both datasets. More precisely, it exceeds the transformer by an average of nearly 1.67%, 1.21%, 1.15%, and 1.49% in terms of accuracy, precision, recall, and \(F_{1}\) score, respectively. The results clearly demonstrate the effectiveness of our approach for time series prediction.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Stock Dataset**} \\ \cline{2-5}  & **Accuracy** & **Precision** & **Recall** & \(F_{1}\) \\ \hline \(SAMA_{5}\) & 42.13\% & 41.44\% & 41.08\% & 39.58\% \\ \(SAMA_{20}\) & 42.63\% & 41.98\% & 41.56\% & 40.10\% \\ \(SAMA_{60}\) & 42.68\% & 41.89\% & 41.50\% & 39.81\% \\ \(SAMA_{5}\)\(|SAMA_{20}\) & 42.71\% & 42.17\% & 41.59\% & 39.90\% \\ \(SAMA_{5}\)\(|SAMA_{60}\) & 42.76\% & 42.20\% & 41.65\% & 40.27\% \\ \(SAMA_{20}\)\(|SAMA_{60}\) & 42.71\% & 42.00\% & 41.63\% & 40.39\% \\ \(SAMA_{5}\)\(|SAMA_{20}|SAMA_{60}\) & 42.75\% & 42.13\% & 41.61\% & 40.12\% \\ \((SAMA_{5}\), \(SAMA_{20}\), \(SAMA_{60})_{mix}\) & **42.82\%** & **42.41\%** & **41.98\%** & **41.38\%** \\ \hline \hline \end{tabular} The best result is indicated in bold and the second best result is underlined. This convention is also adopted in the following table. Furthermore, \(|\) denotes the combination of different scales.  
\end{table}
Table 2: Performance of multi-scale \(SAMA\) indicator fusion (stock dataset).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Air Quality Dataset**} \\ \cline{2-5}  & **Accuracy** & **Precision** & **Recall** & \(F_{1}\) \\ \hline \(SAMA_{5}\) & 69.66\% & 46.22\% & 46.35\% & 46.26\% \\ \(SAMA_{20}\) & 67.86\% & 45.03\% & 45.39\% & 45.20\% \\ \(SAMA_{60}\) & 69.40\% & 45.88\% & 45.73\% & 45.74\% \\ \(SAMA_{5}\)\(|SAMA_{20}\) & 68.83\% & 45.65\% & 45.78\% & 45.69\% \\ \(SAMA_{5}\)\(|SAMA_{60}\) & 70.15\% & 46.55\% & 46.94\% & 46.67\% \\ \(SAMA_{20}\)\(|SAMA_{60}\) & 69.78\% & 46.19\% & 45.81\% & 45.86\% \\ \(SAMA_{5}\)\(|SAMA_{20}|SAMA_{60}\) & 69.40\% & 45.88\% & 46.01\% & 45.94\% \\ \((SAMA_{5}\), \(SAMA_{20}\), \(SAMA_{60})_{mix}\) & **71.64\%** & **47.45\%** & **47.75\%** & **47.58\%** \\ \hline \hline \end{tabular} The best result is indicated in bold and the second best result is underlined. This convention is also adopted in the following table. Furthermore, \(|\) denotes the combination of different scales.  
\end{table}
Table 3: Performance of multi-scale \(SAMA\) indicator fusion (air quality dataset).