# Scale-BOSS-MR: Scalable Time Series Classification Using Multiple Symbolic Representations +
## Appendix A  
The appendix shows detailed accuracy and total execution time results for all the methods and datasets. The detailed tables help compare the proposed methods with other methods proposed in the literature, as well as compare the proposed methods against each other on a per-dataset basis. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline
**Dataset** & **Crop** & **FordA** & **FordB** & **HandOutlines** & **Non InvasiveFetHiCCTonax1** & **Non InvasiveFetHiCCTonax2** & **PhallingsOutlinesCorrect** & **TwoPatterns** \\
**Algorithm** & & & & & & & & & & \\ \hline SIMR-RF-W0-trend-D1-BC & 0.705 & 0.911 & 0.728 & 0.914 & 0.900 & 0.930 & 0.818 & 0.952 \\ SIMR-RF-W0-trend-D1-BC & 0.70 & 0.914 & 0.740 & 0.835 & 0.894 & 0.922 & 0.814 & 0.952 \\ SIMR-RF-W0-trend-D3-BC & 0.709 & 0.917 & 0.738 & 0.911 & 0.893 & 0.925 & 0.817 & 0.945 \\ SIMR-RF-W0-trend-D4-BC & 0.709 & 0.910 & 0.741 & 0.895 & 0.884 & 0.915 & 0.805 & 0.916 \\ SIMR-RF-W0-trend-D5-BC & 0.708 & 0.919 & 0.742 & 0.792 & 0.898 & 0.924 & 0.812 & 0.947 \\ SIMR-RF-W0-trend-D6-BC & 0.703 & 0.914 & 0.741 & 0.892 & 0.894 & 0.919 & 0.805 & 0.924 \\ SIMR-RF-W0-trend-D4-BC & 0.707 & 0.912 & 0.759 & 0.784 & 0.907 & 0.920 & 0.793 & 0.929 \\ SIMR-MLP & 0.576 & 0.872 & 0.695 & 0.776 & 0.796 & 0.798 & 0.774 & 0.941 \\ SIMR-MLP-W0-trend-D4-D4 & 0.641 & 0.892 & 0.794 & 0.799 & 0.859 & 0.866 & 0.779 & 0.950 \\ SIMR-RF-RF-W0-trend-D4-BC & 0.634 & 0.883 & 0.717 & 0.838 & 0.780 & 0.823 & 0.765 & 0.928 \\ SIMR-RF-W0-trend-D4-BC & 0.683 & 0.924 & 0.768 & 0.811 & 0.845 & 0.858 & 0.774 & 0.936 \\ SIMR-RF-D4-BC-VS & 0.550 & 0.833 & 0.668 & 0.681 & 0.702 & 0.771 & 0.712 & 0.902 \\ SIMR-RF-R-K-K-K-K00S-VS-W0-trend-D4-DC & 0.608 & 0.883 & 0.720 & 0.686 & 0.773 & 0.828 & 0.717 & 0.918 \\ \hline \end{tabular}
\end{table} 
Table 10: Accuracy of the classifiers without multiple window configurations. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline
**Dataset** & **Crop** & **FordA** & **FordB** & **HandOutlines** & **Non InvasiveFetHiCCTonax1** & **Non InvasiveFetHiCCTonax2** & **PhallingsOutlinesCorrect** & **TwoPatterns** \\
**Algorithm** & & & & & & & & \\ \hline SIMR-RF-W0-trend-D1-BC & 87.343 & 135.100 & 122.949 & 194.351 & 152.298 & 151.977 & 11.589 & 38.516 \\ SIMR-RF-W0-trend-D1-BC & 86.862 & 122.240 & 127.212 & 184.951 & 140.944 & 139.074 & 10.028 & 30.200 \\ SIMR-RF-W0-trend  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline
**Dataset** & **Crop** & **FordA** & **FordB** & **HandOutlines** & **Non InvasiveFetailECThorax** & **Non InvasiveFetailECThorax2** & **PhalingsOutlinesConrect** & **TwoPatterns** \\
**Algorithm** & & & & & & & & & \\ \hline SMR-MB-K-MOS-VS-W11-D0-BC & 86.356 & 20.894 & 1 \begin{table}
\begin{tabular}{l c c c c c c c c} \hline
**Dataset** & **Crop** & **FordA** & **FordB** & **HandOutlines** & **NonInvasiveFetalICCTbaerx** & **NonInvasiveFetalICCTbaerx2** & **PhallagesOutlinesCorrect** & **TwoPatterns** \\
**Algorithm** & & & & & & & & \\ \hline SRMR-RF-W10-Dc-trend-hjgram & 272.975 & 200.885 & 230.631 & 333.051 & 270.118 & 274.105 & 22.243 & 83.030 \\ SRMR-RF-W11-Dc-trend-hjgram & 402.083 & 206.743 & 187.194 & 255.963 & 219.389 & 212.974 & 20.059 & 104.887 \\ SRMR-RF-W11-Dc-trend-hjgram & 196.900 & 128.388 & 117.495 & 158.499 & 132.244 & 129.837 & 12.071 & 55.198 \\ SRMR-RF-W11-Dc-trend-hjgram & 328.484 & 257.080 & 235.637 & 343.221 & 281.788 & 279.821 & 24.589 & 95.554 \\ SRMR-RF-W14-Dc-trend-hjgram & 179.336 & 176.986 & 156.272 & 232.769 & 186.544 & 186.198 & 16.465 & 58.888 \\ SRMR-RF-W16-btrend-hjgram & 548.205 & 564.149 & 514.789 & 815.115 & 639.852 & 621.876 & 50.155 & 178.285 \\ \hline \end{tabular}
\end{table} 
Table 4: Total execution time results with different windows and dilation configurations.