# Time-Series Classification Based on Fusion Features of Sequence and Visualization
## 4 Experiment
### Sub-Model Evaluation
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **TSC-FF** & **LSTM-A** & **LSTM** & **CNN-A** & **CNN** \\ \hline ACSF1 & 0.8552 & **0.8278** & 0.7800 & **0.8660** & 0.8400 \\ Adiac & **0.7875** & **0.7684** & 0.6199 & **0.7399** & 0.6513 \\ ArrowHead & 0.8025 & **0.8586** & 0.7580 & **0.8185** & 0.6822 \\ Beef & 0.8444 & **0.8704** & 0.8222 & 0.7852 & 0.7963 \\ BeetleFly & **0.8889** & **0.8333** & 0.6111 & **0.8121** & 0.5023 \\ BirdChicken & 0.9222 & **0.9444** & 0.8423 & 0.7342 & 0.7932 \\ BME & 0.8921 & **0.8400** & 0.8333 & **0.9200** & 0.9133 \\ Car & 0.9500 & **0.9667** & 0.8852 & **0.8667** & 0.8222 \\ CBF & **1.0000** & **0.9989** & 0.8593 & **1.0000** & 0.6716 \\ Chinatown & **0.9861** & 0.9478 & 0.9536 & **0.9652** & 0.9217 \\ ChlCon & **0.7246** & **0.6892** & 0.6399 & **0.7127** & 0.6306 \\ CinCECGtorso & 0.9511 & **0.8719** & 0.8300 & **0.9778** & 0.8911 \\ Coffee & **1.0000** & **1.0000** & 1.0000 & **1.0000** & 1.0000 \\ Computers & **0.7200** & **0.5600** & 0.5400 & **0.7200** & 0.6022 \\ CricketX & **0.8077** & **0.7923** & 0.7356 & **0.7694** & 0.7279 \\ CricketY & **0.8000** & 0.7556 & 0.6400 & **0.7410** & 0.5761 \\ CricketZ & **0.8282** & **0.7926** & 0.5499 & **0.7667** & 0.5627 \\ Crop & 0.7622 & **0.7636** & 0.7589 & **0.7712** & 0.7640 \\ DiaSizeRed & **0.9471** & **0.8800** & 0.5673 & **0.9239** & 0.4982 \\ DislPhaOutCor & **0.7661** & 0.7460 & 0.7621 & **0.7661** & 0.5968 \\ DisPhaOutAgeGro & **0.7120** & **0.6960** & 0.4000 & **0.7040** & 0.4240 \\ DisPhaTW & 0.6875 & **0.6960** & 0.4960 & **0.6480** & 0.4800 \\ Earthquakes & **0.8160** & **0.8080** & 0.7200 & 0.7360 & 0.7440 \\ ECG200 & **0.8916** & **0.8724** & 0.8245 & **0.8869** & 0.8046 \\ ECG500 & 0.9481 & **0.9427** & 0.7230 & **0.9486** & 0.9442 \\ ECGFiveDays & 0.9991 & 0.7356 & 0.8052 & **0.9998** & 0.7935 \\ ElectricDevices & 0.7413 & **0.7420** & 0.7381 & **0.6938** & 0.6757 \\ EOGHorSig & **0.8358** & **0.7818** & 0.7569 & **0.8011** & 0.7707 \\ EOGVerSig & **0.7722** & **0.7541** & 0.7348 & 0.7569 & 0.7486 \\ EthLevel & **0.8442** & **0.8085** & 0.7864 & **0.8182** & 0.7485 \\ FaceAll & 0.8705 & **0.8567** & 0.7344 & **0.8824** & 0.7879 \\ FaceFour & **0.9091** & **0.8608** & 0.2785 & **0.8861** & 0.2785 \\ FacesUCR & 0.9593 & **0.9786** & 0.8618 & **0.8390** & 0.8119 \\ FiftyWords & **0.8041** & **0.7822** & 0.4318 & **0.7922** & 0.4320 \\ Fish & **0.9886** & **0.9471** & 0.8019 & **0.9134** & 0.8338 \\ FordA & **1.0000** & **1.0000** & 1.0000 & **1.0000** & 1.0000 \\ FordB & **1.0000** & **1.0000** & 1.0000 & **1.0000** & 1.0000 \\ FreRegTra & **0.9989** & 0.9940 & 0.9961 & **0.9979** & 0.9965 \\ FreSmaTra & **0.9668** & **0.9660** & 0.9547 & **0.9642** & 0.9323 \\ GunPoint & **1.0000** & **0.9556** & 0.6889 & **0.9852** & 0.5333 \\ GPAgSpa & **1.0000** & **1.0000** & 1.0000 & **1.0000** & 1.0000 \\ GPMalVerFem & **1.0000** & **1.0000** & 1.0000 & **1.0000** & 0.9968 \\ GPOldVerYou & **0.9264** & **0.9244** & 0.9199 & **0.9011** & 0.8507 \\ Ham & **0.7128** & **0.6489** & 0.6170 & **0.7021** & 0.4787 \\ HandOutlines & **0.9069** & **0.8679** & 0.3634 & **0.8739** & 0.6366 \\ Haptics & **0.5213** & 0.3863 & 0.3971 & **0.4621** & 0.2986 \\ Herring & **0.6491** & **0.6316** & 0.6140 & **0.5088** & 0.3860 \\ HouTwe & **0.9613** & **0.9412** & 0.8824 & **0.9664** & 0.9496 \\ InlineSkate & 0.4616 & 0.3747 & 0.4404 & **0.4694** & 0.4040 \\ InsEPCRRegTra & **1.0000** & **1.0000** & 1.0000 & **1.0000** & 1.0000 \\ InsEPCSmaTra & 0.5150 & **0.4739** & 0.4626 & **0.5238** & 0.4610 \\ InsWinSou & **0.6269** & **0.5752** & 0.5196 & **0.6066** & 0.3889 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Accuracy comparison of TSC-FF with sub-models on 112 data sets.