# Scale-BOSS-MR: Scalable Time Series Classification Using Multiple Symbolic Representations +
## Appendix A
\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-RidgeC-W-non-D-D-UC & 0.579 & 0.885 & 0.665 & 0.668 & 0.743 & 0.796 & 0.733 & 0.877 \\ SRMR-RidgeC-W-non-D-D-UC & 0.622 & 0.914 & 0.743 & 0.649 & 0.830 & 0.843 & 0.752 & 0.891 \\ SRMR-RidgeC-W-non-D-DC-UC & 0.770 & 0.936 & 0.721 & 0.886 & 0.521 & 0.940 & 0.787 & 0.960 \\ SRMR-RidgeC-W-non-D-D-UC & 0.711 & 0.923 & 0.715 & 0.886 & 0.917 & 0.997 & 0.958 \\ SRMR-RidgeC-W-non-D-DC-UC & 0.701 & 0.923 & 0.749 & 0.867 & 0.910 & 0.933 & 0.781 & 0.960 \\ SRMR-RidgeC-W-non-D-DC-UC & 0.696 & 0.915 & 0.728 & 0.888 & 0.986 & 0.912 & 0.779 & 0.953 \\ SRMRMR-RidgeC-W-non-D-DC-UC & 0.701 & 0.934 & 0.748 & 0.770 & 0.909 & 0.929 & 0.779 & 0.959 \\ SRMRMR-RidgeC-W-non-D-UC-UC & 0.699 & 0.924 & 0.731 & 0.832 & 0.907 & 0.926 & 0.778 & 0.936 \\ SRMRMR-RidgeC-W-non-D-UC-UC & 0.648 & 0.921 & 0.737 & 0.746 & 0.892 & 0.913 & 0.735 & 0.929 \\ \hline \end{tabular}
\end{table} 
Table 5: Total execution time using ridge regression classifier without multiple window configurations. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline
**Dataset** & **Crop** & **FordA** & **FordB** & **HandOutlines** & **Non InvasiveFeat1 ECCThorax1** & **Non InvasiveFeat1 ECCThorax2** & **PhalangesOutlinesConrect** & **TwoPatterns** \\
**Algorithm** & & & & & & & & & \\ \hline SMR-RidgeC-VW11-rend-D-R6: & 0.750 & 0.944 & 0.821 & 0.941 & 0.922 & 0.944 & 0.790 & 0.941 \\ SMR-RidgeC-VW11-rend-D-R6: & 0.729 & 0.938 & 0.817 & 0.745 & 0.885 & 0.900 & 0.812 & 0.924 \\ SMR-RidgeC-VW11-rend-D-R6: & 0.704 & 0.932 & 0.801 & 0.714 & 0.868 & 0.884 & 0.779 & 0.930 \\ SMR-RidgeC-VW11-rend-D6: & 0.729 & 0.942 & 0.803 & 0.927 & 0.920 & 0.940 & 0.819 & 0.956 \\ SMR-RidgeC-VW11-rend-D6: & 0.729 & 0.988 & 0.794 & 0.953 & 0.912 & 0.959 & 0.817 & 0.906 \\ SMR-RidgeC-VW11-rend-D6: & 0.721 & 0.931 & 0.807 & 0.754 & 0.869 & 0.887 & 0.775 & 0.895 \\ SMR-RidgeC-VW1-rend-D6: & 0.747 & 0.939 & 0.810 & 0.938 & 0.936 & 0.958 & 0.830 & 0.984 \\ SMR-RidgeC-VW1-rend-D6: & 0.699 & 0.524 & 0.241 & 0.832 & 0.907 & 0.925 & 0.788 & 0.936 \\ \hline \end{tabular}
\end{table} 
Table 10: Total execution time using ridge regression classifier with different windows’ configurations. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline
**Dataset** & **Crop** & **FordA** & **FordB** & **HandOutlines** & **Non InvasiveFeat1 ECCThorax1** & **Non InvasiveFeat1 ECCThorax2** & **PhalangesOutlinesConrect** & **TwoPatterns** \\
**Algorithm** & & & & & & & & \\ \hline SMR-RidgeC-VW11-rend-D6-R6: & 0.750 & 0.944 & 0.821 & 0.941 & 0.922 & 0.944 & 0.790 & 0.941 \\ SMR-RidgeC-VW11-rend-D6-R6: & 0.729 & 0.938 & 0.817 & 0.745 & 0.885 & 0.900 & 0.812 & 0.924 \\ SMR-RidgeC-VW11-rend-D6-R6: & 0.704 & 0.932 & 0.801 & 0.714 & 0.868 & 0.884 & 0.779 & 0.930 \\ SMR-RidgeC-VW11-rend-D6-R6: & 0.729 & 0.942 & 0.803 & 0.927 & 0.920 & 0.940 & 0.819 & 0.956 \\ SMR-RidgeC-VW11-rend-D6-R6: & 0.729 & 0.942 & 0.803 & 0.927 & 0.926 & 0.929 & 0.817 & 0.906 \\ SMR-RidgeC-VW11-rend-D6: & 0.721 & 0.931 & 0.807 & 0.754 & 0.869 & 0.887 & 0.775 & 0.895 \\ SMR-RidgeC-VW1-rend-D6-R6: & 0.747 & 0.939 & 0.801 & 0.938 & 0.936 & 0.958 & 0.830 & 0.984 \\ SMR-RidgeC-VW1-rend-D6: & 0.699 & 0.524 & 0.241 & 0.832 & 0.907 & 0.925 & 0.788 & 0.936 \\ \hline \end{tabular}
\end{table} 
Table 8: Accuracy using ridge regression classifier with different windows’ configurations.