# BIMO: Bootstrap Inter-Intra Modality at Once Unsupervised Learning for Multivariate Time Series
## 3 Results and Discussion
### Univariate Time Series
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{3}{c}{**Unsupervised**} & \multicolumn{3}{c}{**Supervised**} \\ \cline{2-9}  & **BIMO** & **USRL** & **DTW** & **BOSS** & **PF** & **HIVE-COTE** & **ITime** \\ \hline Lightning7 & **0.819** & 0.795 & 0.726 & 0.685 & 0.822 * & 0.740 & 0.808 \\ Mallat & 0.956 & **0.964 * & 0.934 & 0.938 & 0.958 & 0.962 & 0.963 \\ Meat & **1.000 * & 0.950 & 0.933 & 0.900 & 0.933 & 0.933 & 0.950 \\ MedicalImages & 0.730 & **0.784** & 0.737 & 0.718 & 0.758 & 0.778 & 0.799 * \\ MidPhaOutAgeGroup & 0.618 & **0.656 * & 0.500 & 0.545 & 0.562 & 0.597 & 0.533 \\ MidPhaOutCorrect & **0.826** & 0.814 & 0.698 & 0.780 & 0.836 * & 0.832 & 0.835 \\ MiddlePhalmaxTW & 0.566 & **0.610 * & 0.506 & 0.545 & 0.529 & 0.571 & 0.513 \\ MoteStrain & **0.871** & **0.871** & 0.835 & 0.879 & 0.902 & 0.933 * & 0.903 \\ NonInvFetECGTho1 & **0.923** & 0.910 & 0.790 & 0.838 & 0.906 & 0.930 & 0.962 * \\ NonInvFetECGTho2 & **0.929** & 0.927 & 0.865 & 0.901 & 0.940 & 0.945 & 0.967 * \\ OliveOil & **0.964 * & 0.900 & 0.833 & 0.867 & 0.867 & 0.900 & 0.867 \\ OSULeaf & 0.729 & **0.831** & 0.591 & 0.955 & 0.827 & 0.979 * & 0.934 \\ PhaOutCorrect & **0.801** & **0.801** & 0.728 & 0.772 & 0.824 & 0.807 & 0.854 * \\ Phoneme & 0.263 & **0.289** & 0.228 & 0.265 & 0.320 & 0.382 * & 0.335 \\ Plane & **1.000 * & 0.990 & **1.000 * & 1.000 * & 1.000 * & 1.000 * & 1.000 * \\ ProPhaOutAgeGroup & **0.863 * & 0.854 & 0.805 & 0.834 & 0.846 & 0.859 & 0.854 \\ ProPhaOutCorrect & **0.878** & 0.859 & 0.784 & 0.849 & 0.873 & 0.880 & 0.931 * \\ ProximalPhalanxTW & 0.814 & **0.824 * & 0.761 & 0.800 & 0.779 & 0.815 & 0.776 \\ RefrigerationDevices & **0.524** & 0.517 & 0.464 & 0.499 & 0.532 & 0.557 * & 0.509 \\ ScreenType & **0.446** & 0.413 & 0.397 & 0.464 & 0.455 & 0.589 * & 0.576 \\ ShapeStint & 0.694 & **0.817** & 0.650 & 1.000 * & 0.776 & 1.000 * & 0.989 \\ ShapesAll & 0.667 & **0.875** & 0.768 & 0.908 & 0.886 & 0.905 & 0.925 * \\ SmaKitAppliances & **0.720** & 0.715 & 0.643 & 0.725 & 0.744 & 0.853 * & 0.779 \\ SonAlBORobSur1 & **0.967 * & 0.897 & 0.725 & 0.632 & 0.846 & 0.765 & 0.884 \\ SonAlBORobSur2 & 0.858 & **0.934** & 0.831 & 0.859 & 0.896 & 0.928 & 0.953 * \\ StarLightCurves & **0.970** & 0.965 & 0.907 & 0.978 & 0.981 & 0.982 * & 0.979 \\ Strawberry & **0.962** & 0.946 & 0.941 & 0.976 & 0.968 & 0.970 & 0.984 * \\ SwedishLeaf & 0.929 & **0.931** & 0.792 & 0.922 & 0.947 & 0.954 & 0.971 * \\ Symbols & 0.960 & **0.965** & 0.950 & 0.967 & 0.962 & 0.974 & 0.982 * \\ SyntheticControl & 0.900 & 0.983 & **0.993** & 0.967 & 0.995 & 0.997 * & 0.997 * \\ ToeSeg1 & 0.917 & **0.952** & 0.772 & 0.939 & 0.925 & 0.982 * & 0.969 \\ ToeSeg2 & **0.891** & 0.885 & 0.838 & 0.962 * & 0.862 & 0.954 & 0.939 \\ Trace & **1.000 * & **1.000 * & **1.000 * & 1.000 * & 1.000 * & 1.000 * & 1.000 * \\ TwoLeadECG & 0.996 & **0.997 * & 0.905 & 0.981 & 0.989 & 0.996 & 0.996 \\ TwoPatterns & **1.000 * & **1.000 * & **1.000 * & 0.993 & 1.000 * & 1.000 * & 1.000 * \\ UWavGesLibAll & **0.958** & 0.941 & 0.892 & 0.939 & 0.972 * & 0.968 & 0.955 \\ UWavGesLibX & 0.802 & **0.811** & 0.728 & 0.762 & 0.829 & 0.840 * & 0.825 \\ UWavGesLibY & 0.712 & **0.735** & 0.634 & 0.685 & 0.762 & 0.765 & 0.769 * \\ UWavGesLibZ & 0.742 & **0.759** & 0.658 & 0.695 & 0.764 & 0.783 * & 0.770 \\ Wafer & **0.996** & 0.993 & 0.980 & 0.995 & 0.996 & 0.999 * & 0.999 * \\ Wine & 0.808 & **0.870 * & 0.574 & 0.741 & 0.569 & 0.778 & 0.667 \\ WordSynonyms & 0.701 & **0.704** & 0.649 & 0.638 & 0.779 * & 0.738 & 0.756 \\ Worms & 0.684 & **0.714** & 0.584 & 0.558 & 0.718 & 0.558 & 0.805 * \\ WormsTwoClass & **0.842 * & 0.818 & 0.623 & 0.831 & 0.784 & 0.779 & 0.792 \\ Yoga & 0.807 & **0.878** & 0.837 & 0.918 * & 0.879 & 0.918 * & 0.906 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._ITime, BIMO, BOSS, and PF, respectively. These results demonstrate that BIMO is superior to existing SOTA unsupervised models and comparable to well-known supervised models.