# Time-Series Classification Based on Fusion Features of Sequence and Visualization
## 4 Experiment
### Integrated Model Evaluation
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Records in Each Category** & **(0,20]** & **(20,50]** & **(50,100]** & **(100,200]** & **(200,500]** & **(500,1000]** & **(1000,+\infty)** \\ \hline \hline \multicolumn{7}{c}{Distance-based methods} \\ \hline DTW & 0.7208 & 0.7186 & 0.7433 & 0.6497 & 0.8156 & 0.7401 & 0.6512 \\ PF & 0.8593 & 0.8311 & 0.8745 & 0.7472 & 0.9003 & 0.2755 & 0.8439 \\ \hline \multicolumn{7}{c}{Feature-based methods} \\ \hline BOSS & 0.8703 & 0.8148 & 0.8463 & 0.7543 & 0.8917 & 0.8174 & 0.8754 \\ ST & 0.8574 & 0.8377 & 0.8417 & 0.7585 & 0.8660 & 0.7634 & 0.8419 \\ STC & 0.8623 & 0.8497 & 0.8676 & 0.8110 & 0.9059 & 0.8337 & 0.9119 \\ WS & 0.8721 & 0.8374 & 0.8350 & 0.7734 & 0.9037 & 0.8217 & 0.9126 \\ TSF & 0.7704 & 0.8077 & 0.8689 & 0.7436 & 0.8927 & 0.8057 & 0.7999 \\ RISE & 0.7998 & 0.7853 & 0.8577 & 0.7483 & 0.8315 & 0.8125 & 0.8938 \\ \hline \multicolumn{7}{c}{Ensemble methods} \\ \hline HCT & 0.8977 & 0.8687 & **0.8903** & 0.8232 & 0.9067 & 0.8265 & 0.9173 \\ CHI & 0.9147 & 0.8543 & 0.8844 & 0.7862 & 0.9021 & 0.9601 & 0.9106 \\ RK & 0.9001 & 0.8484 & 0.8679 & 0.8074 & 0.9085 & 0.1955 & 0.9195 \\ C2 & 0.7721 & 0.7638 & 0.8347 & 0.7375 & 0.8532 & 0.7919 & 0.8838 \\ \hline \multicolumn{7}{c}{Deep learning methods} \\ \hline RN & 0.8060 & **0.8728** & 0.8081 & 0.7957 & 0.9056 & 0.8477 & 0.9109 \\ FCN & 0.7936 & 0.8358 & 0.7660 & 0.7710 & 0.8741 & 0.8260 & 0.8373 \\ Ict & **0.9009** & 0.8597 & 0.8439 & **0.8137** & **0.9127** & **0.9221** & **0.9301** \\ \hline TSC-FF & 0.8333 & 0.8640 & 0.8324 & 0.8027 & 0.8787 & 0.8096 & 0.9138 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average accuracy of all seven methods with different amounts of records in each category. Bold values denote the model with the best performance. \begin{table}
\begin{tabular}{l c c c} \hline \hline  & **GASF-GADF-MTF** & **TFRP** & **TSC-FF** \\ \hline Adiac & 0.6270 & **0.7954** & 0.7875 \\ Beef & 0.7670 & 0.6333 & **0.8444** \\ CBF & 0.9910 & N/A & **1.0000** \\ Coffee & **1.0000** & 0.9643 & **1.0000** \\ ECGFiveDays & 0.9100 & 0.8955 & **0.9991** \\ FaceAll & 0.7630 & 0.7101 & **0.8705** \\ FaceFour & **0.9320** & 0.7841 & 0.9091 \\
50 words & 0.6990 & 0.5626 & **0.8041** \\ fish & 0.8860 & 0.8800 & **0.9886** \\ Gun Point & 0.9200 & 0.9800 & **1.0000** \\ Lighting2 & 0.8860 & 0.8525 & **1.0000** \\ Lighting7 & 0.7400 & 0.6849 & **0.8115** \\ OliveOil & 0.8000 & 0.8667 & **0.8667** \\ OSULeaf & 0.6420 & 0.9298 & **0.9695** \\ SwedishLeaf & 0.9350 & **0.9504** & 0.8779 \\ SyntheticControl & 0.9930 & N/A & **1.0000** \\ Trace & **1.0000** & N/A & **1.0000** \\ Two Patterns & 0.9090 & N/A & **1.0000** \\ wafer & **1.0000** & 0.9998 & **1.0000** \\ yoga & 0.8040 & 0.8587 & **0.9007** \\ \hline Average Rank & 2.0500 & 2.3125 & 1.2500 \\ \hline MPSE & \(2.6500\times 10^{-2}\) & \(3.1100\times 10^{-2}\) & \(9.8900\times 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Accuracy comparison of visual feature-based methods on 20 data sets in UCR. Bold values denote model with the best performance.