# SMM: Leveraging Metadata for Contextually Salient Multi-Variate Motif Discovery
## 5 Experimental Evaluation
### Results for the Mocap and Energy Datasets
#### 5.5.1 Accuracy
Table 6 compares the accuracies for time series where the recurring patterns are variate-aligned (or _v-aligned_) vs. those where the motifs can appear on different (structurally compatible) subsets of variates (i.e., _v-structure_). As we would expect, the overall accuracy is higher when the patterns occur on the same variates (i.e., the _v-structure_ has somewhat lower results than the default _v-aligned_ case); but, critically, MPC performs poorly when the recurring patterns are not variate-aligned. In contrast, SMM-s is able to identify a larger number of such patterns. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{F-Score MoCap (1 Motif \(k=5\), \(f=50\)-\(100\%\))} \\ \cline{2-10}
**Instances** & \multicolumn{2}{c}{**RW 10\%**} & \multicolumn{2}{c}{**RW 50\%**} & \multicolumn{2}{c}{**RW 75\%**} & \multicolumn{2}{c}{**RW 100\%**} \\ \cline{2-10}
**Overlap** & **SMM-s** & **MPC** & **SMM-s** & **MPC** & **SMM-s** & **MPC** & **SMM-s** & **MPC** \\ \hline
50\% & **0.69** & 0.33 & **0.70** & 0.30 & **0.66** & 0.33 & **0.57** & 0.32 \\
75\% & **0.74** & 0.31 & **0.73** & 0.25 & **0.67** & 0.24 & **0.57** & 0.22 \\
90\% & **0.75** & 0.27 & **0.74** & 0.19 & **0.68** & 0.20 & **0.57** & 0.17 \\
100\% & **0.70** & 0.18 & **0.67** & 0.15 & **0.61** & 0.15 & **0.51** & 0.14 \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Instances**} & \multicolumn{2}{c}{**RW 10\%**} & \multicolumn{2}{c}{**RW 50\%**} & \multicolumn{2}{c}{**RW 75\%**} & \multicolumn{2}{c}{**RW 100\%**} \\ \cline{2-10}
**Overlap** & **SMM-s** & **MPC** & **SMM-s** & **MPC** & **SMM-s** & **MPC** & **SMM-s** & **MPC** \\ \hline
50\% & **0.66** & 0.14 & **0.60** & 0.17 & **0.50** & 0.15 & **0.43** & 0.14 \\
75\% & **0.52** & 0.10 & **0.45** & 0.11 & **0.37** & 0.09 & **0.32** & 0.09 \\
90\% & **0.45** & 0.07 & **0.41** & 0.07 & **0.34** & 0.06 & **0.29** & 0.07 \\
100\% & **0.34** & 0.05 & **0.30** & 0.06 & **0.24** & 0.05 & **0.20** & 0.05 \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Instances**} & \multicolumn{2}{c}{**RW 10\%**} & \multicolumn{2}{c}{**RW 50\%**} & \multicolumn{2}{c}{**RW 75\%**} & \multicolumn{2}{c}{**RW 100\%**} \\ \cline{2-10}
**Overlap** & **SMM-s** & **MPC** & **SMM-s** & **MPC** & **SMM-s** & **MPC** & **SMM-s** & **MPC** \\ \hline
50\% & **0.80** & 0.55 & **0.66** & 0.49 & **0.59** & 0.49 & **0.55** & 0.48 \\
75\% & **0.60** & 0.56 & **0.51** & 0.48 & **0.47** & 0.43 & **0.45** & 0.40 \\
90\% & **0.53** & 0.48 & **0.47** & 0.38 & **0.44** & 0.32 & **0.43** & 0.26 \\
100\% & **0.41** & 0.14 & **0.37** & 0.14 & **0.35** & 0.14 & **0.34** & 0.13 \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Instances**} & \multicolumn{2}{c}{**RW 10\%**} & \multicolumn{2}{c}{**RW 50\%**} & \multicolumn{2}{c}{**RW 75\%**} & \multicolumn{2}{c}{**RW 100\%**} \\ \cline{2-10}
**Overlap** & **SMM-s** & **MPC** & **SMM-s** & **MPC** & **SMM-s** & **MPC** & **SMM-s** & **MPC** \\ \hline
50\% & **0.62** & 0.29 & **0.57** & 0.30 & **0.50** & 0.31 & **0.41** & 0.28 \\
75\% & **0.53** & 0.23 & **0.47** & 0.24 & **0.41** & 0.23 & **0.36** & 0.19 \\
90\% & **0.47** & 0.16 & **0.42** & 0.18 & **0.37** & 0.16 & **0.32** & 0.12 \\
100\% & **0.41** & 0.06 & **0.37** & 0.09 & **0.32** & 0.09 & **0.28** & 0.07 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: F-scores for time series with recurring patterns for varying number of recurrence counts using MoCap (default parameters, various \(k\)). **Scalability against the Number of Motifs. ** In all the results presented so far, the time series has a single unique motif. In the next set of experiments, we (significantly) increase the number of (MoCap) motifs injected to each multi-variate series to 10. Note that the injected motifs may include common patterns that can potentially confuse the motif search algorithms. Table 7 presents the results.