# BIMO: Bootstrap Inter-Intra Modality at Once Unsupervised Learning for Multivariate Time Series
## 3 Results and Discussion
### Univariate Time Series  
We validated the proposed BIMO method's performance using the 85 initially released UCR datasets, which are representative univariate time-series datasets [25]. (1) We compared the BIMO method's performance to that of existing SOTA unsupervised models, (2) with the existing SOTA supervised models, and (3) compared the performance depending on combinations of the auxiliary networks. **Overall Performance:** In terms of performance, we compared the proposed BIMO method with unsupervised models for time series, i.e., USRL (which utilizes triplet loss) [23], DTW (which employs a kernel-based estimation method) [37], and RWS (which uses a similarity matrix) [38], as shown in Table 2. \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 Adiac & **0.760** & 0.716 & 0.604 & 0.765 & 0.734 & 0.811 & 0.836 * \\ ArrowHead & 0.814 & **0.829** & 0.703 & 0.834 & 0.875 * & 0.863 & 0.829 \\ Beef & **0.800** & 0.700 & 0.633 & 0.800 & 0.720 & 0.933 * & 0.700 \\ BeetleFly & 0.850 & **0.900** & 0.700 & 0.900 & 0.875 & 0.950 * & 0.850 \\ BirdChicken & **0.900** & 0.800 & 0.750 & 0.950 * & 0.865 & 0.867 & 0.950 * \\ Car & **0.917 * & 0.817 & 0.733 & 0.833 & 0.847 & 0.867 & 0.900 \\ CBF & **0.998** & 0.994 & 0.997 & 0.998 & 0.993 & 0.999 * & 0.998 \\ ChlCon & 0.635 & **0.782** & 0.648 & 0.661 & 0.634 & 0.712 & 0.875 * \\ CinCECGrso & **0.757** & 0.740 & 0.651 & 0.887 & 0.934 & 0.996 * & 0.851 \\ Coffee & **1.000 * & **1.000 * & **1.000 * & 1.000 * & 1.000 * & 1.000 * & 1.000 * \\ Computers & 0.681 & 0.628 & **0.700** & 0.756 & 0.644 & 0.760 & 0.812 * \\ CricketX & 0.750 & **0.777** & 0.754 & 0.736 & 0.802 & 0.823 & 0.867 * \\ CricketY & 0.716 & **0.767** & 0.744 & 0.754 & 0.794 & 0.849 & 0.851 * \\ CricketZ & 0.758 & **0.764** & 0.754 & 0.746 & 0.801 & 0.831 & 0.859 * \\ DiaSizRed & 0.977 & **0.993 * & 0.967 & 0.931 & 0.966 & 0.941 & 0.931 \\ DisPhaOutAgeGroup & 0.743 & 0.734 & **0.770 * & 0.748 & 0.731 & 0.763 & 0.727 \\ DisPhazOutCorrect & **0.786** & 0.768 & 0.717 & 0.728 & 0.793 & 0.772 & 0.794 * \\ DistalPhalanxTW & **0.684 * & 0.676 & 0.590 & 0.676 & 0.660 & 0.683 & 0.676 \\ Earthqu & **0.765 * & 0.748 & 0.719 & 0.748 & 0.754 & 0.748 & 0.741 \\ ECG200 & **0.900** & **0.900** & 0.770 & 0.870 & 0.909 & 0.850 & 0.910 * \\ ECG5000 & **0.940** & 0.936 & 0.924 & 0.941 & 0.937 & 0.946 * & 0.941 \\ ECGFiveDays & **1.000 * & **1.000 * & 0.768 & 1.000 * & 0.849 & 1.000 * & 1.000 * \\ ElectricDevices & 0.632 & **0.732** & 0.602 & 0.799 * & 0.706 & 0.770 & 0.723 \\ FaceAll & **0.839** & 0.802 & 0.808 & 0.782 & 0.894 * & 0.803 & 0.804 \\ FaceFour & 0.841 & **0.875** & 0.830 & 1.000 * & 0.974 & 0.955 & 0.966 \\ FacesUCR & **0.948** & 0.918 & 0.905 & 0.957 & 0.946 & 0.963 & 0.973 * \\ FiftyWords & **0.783** & 0.780 & 0.690 & 0.705 & 0.831 & 0.809 & 0.842 * \\ Fish & **0.959** & 0.880 & 0.823 & 0.989* & 0.935 & 0.989 * & 0.983 * \\ FordA & 0.850 & **0.935** & 0.555 & 0.930 & 0.855 & 0.964 * & 0.948 \\ FordB & 0.714 & **0.810** & 0.620 & 0.711 & 0.715 & 0.823 & 0.937 * \\ GunPoint & **1.000 * & 0.993 & 0.907 & 1.000 * & 0.997 & 1.000 * & 1.000 * \\ Ham & **0.740 * & 0.695 & 0.467 & 0.667 & 0.660 & 0.667 & 0.714 \\ HandOutlines & **0.924** & 0.922 & 0.881 & 0.903 & 0.921 & 0.932 & 0.960 * \\ Haptics & **0.510** & 0.455 & 0.377 & 0.461 & 0.445 & 0.519 & 0.568 * \\ Herring & **0.703 * & 0.578 & 0.531 & 0.547 & 0.580 & 0.688 & 0.703 * \\ InlineSkate & 0.372 & **0.447** & 0.384 & 0.516 & 0.542 * & 0.500 & 0.486 \\ InsWinbeatSound & **0.630** & 0.623 & 0.355 & 0.523 & 0.619 & 0.655 * & 0.635 \\ ItalyPowerDemand & **0.963** & 0.925 & 0.950 & 0.909 & 0.967 & 0.963 & 0.968 * \\ LarKitAppliances & **0.866** & 0.848 & 0.795 & 0.765 & 0.782 & 0.864 & 0.907 * \\ Lightning2 & 0.883 & **0.918 * & 0.869 & 0.836 & 0.866 & 0.820 & 0.803 \\ \hline \hline \end{tabular}
\end{table}