# Scale-BOSS-MR: Scalable Time Series Classification Using Multiple Symbolic Representations +
## 4 Evaluation
### Evaluation of SBMR with D0 and W0 Configurations  
From Table 4 and Figure 3, we can see the following.  
1. BOSS-RF starts with an approximately 0.8 accuracy and 9.7 s of mean total execution time;
2. SBMR-RF-W0-trend-D0-UG achieves a 0.827 accuracy with 19.7 s of mean total execution time. This means that adding trend information gives a boost in accuracy, but at the expense of doubling the mean total execution time;
3. SBMR-RF-W0-trend-D6-BG achieves a 0.849 accuracy with 52 s of mean total execution time. This means that even moderate dilation helps to boost accuracy at the expense of further increasing the total execution time;
4. SBMR-RF-W0-trend-D3-BG achieves an accuracy of 0.857 with 70 s of mean total execution time.  
From the above, we can conclude the following:  
1. Bigrams with dilation help to improve the accuracy of the method;
2. D6 (described in Table 3) performs better than D4 (with little difference) and D5 (with slightly bigger difference) configurations;
3. However, D3 (as described in Table 3) performs best (but with slightly worse total execution time compared to D6 and D4 configurations). This is expected, as the representations from D3 are more in number compared to those of D4 and D6, while it seems that the receptive field introduced by D4 and D6 is not sufficient for the algorithms to discriminate between time series' classes effectively. It must be noted that in these configurations, dilation applies once to the time series, as these use the W0 window configuration. This is further supported by the results reported by D1 and D2, which incorporate the representations produced by D3 and D6, thus achieving nearly the same accuracy, but with considerably increased total execution time;
4. An additional finding is that representations with bigrams (BG) are in general more effective compared to those using unigrams (UG), in these configurations of the framework.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Accuracy\_Mean** & **Accuracy\_std** & **Total\_Time\_Mean** & **Total\_Time\_std** \\
**Algorithm** & & & \\ \hline SBMR-RF-W8-trend-D6-BG & 0.875 & 0.079 & 489.0 & 234.5 \\ SBMR-RF-W14-trend-D3-BG & 0.865 & 0.077 & 230.8 & 105.2 \\ SBMR-RF-W10-trend-D6-BG & 0.865 & 0.073 & 217.9 & 100.4 \\ SBMR-RF-W14-trend-D6-BG & 0.863 & 0.074 & 149.2 & 68.3 \\ SBMR-RF-W11-trend-D3-BG & 0.859 & 0.073 & 201.1 & 103.7 \\ SBMR-RF-W11-trend-D6-BG & 0.857 & 0.073 & 116.3 & 54.1 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Accuracy and execution time for combinations of window and dilation configurations.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Accuracy\_Mean** & **Accuracy\_std** & **Total\_Time\_Mean** & **Total\_Time\_std** \\
**Algorithm** & & & & \\ \hline SBMR-MB-K-BOSS-VS-W1-noTrend-D0-BG & 0.767 & 0.106 & 32.5 & 24.9 \\ SBMR-MB-K-BOSS-VS-W1-noTrend-D0-UG & 0.760 & 0.113 & 23.1 & 15.4 \\ \hline \hline \end{tabular}
\end{table}
Table 5: _Cont._Table 5 provides evidence on window configurations with no dilation. Contrary to what is reported in Table 4, here, representations with unigrams seem to be more effective compared to those with bigrams, while it is clear that adding representations with trend increases the mean classification accuracy. The W8 configuration reports the best mean accuracy among others, although with increased total execution time. This is so, given that W8 incorporates many window configurations, all with step 1. In contrast, W10 is an indicative case with slightly fewer window configurations, and step 2 achieves worst accuracy, but with significantly reduced mean total execution time.