# Random Subspace Ensembles of Fully Convolutional Network for Time Series Classification
## 3 Experiments
This illustrates building neural network ensembles by RSM manner is an effective way to solve TSC problem. RSM offers various combinations as input and thus trains many FCN classifiers which focus on different features. Compared with the multimodal neural networks like MCNN and MFCN, RSE-FCN can integrate more comprehensive information, so it is no wonder that overwhelming performance can be achieved. RSE-FCN is also more competitive than the notable non-deep-learning ensemble model COTE, showing the advantage of employing powerful deep learning method as individuals. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **EnRS-Bagging** & **EnRS-Boosting** & **XG-SF** & **RSE-FCN** \\ \hline Adiac & 0.629 & 0.661 & 0.762 & **0.867** \\ Beef & 0.671 & 0.653 & **0.833** & 0.800 \\ Coffee & 0.956 & 0.968 & 0.929 & **1.000** \\ DiaSizeRed & 0.933 & 0.886 & **1.000** & 0.980 \\ IWPDmd & 0.957 & 0.957 & **0.985** & 0.959 \\ Lighting7 & 0.710 & 0.682 & 0.757 & 0.863 \\ MoteStrain & 0.878 & 0.871 & **0.950** & 0.908 \\ Symbols & 0.865 & 0.841 & 0.920 & **0.980** \\ Trace & **1.000** & **1.000** & **1.000** & 0.990 \\ TwoLeadECG & 0.946 & 0.953 & **1.000** & 0.980 \\ AVERAGE & 0.854 & 0.847 & 0.914 & **0.933** \\ BEST & 1 & 1 & **6** & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison on common datasets with the models based on shapelets information. Figure 4: Critical difference diagram of models involved in comparison. RSE-FCN achieves higher accuracy and more winning times than COTE and EnRS. XG-SF gives more top result than RSE-FCN but has less average accuracy, which also means it is not as robust as RSE-FCN. The three ones are methods which learn Shapelets information. Consequently, it suggests our method makes use of discriminative local information successfully, as well. Although simple and rough the converting process is, it retains the key subsequences and eliminates the information weakly related to TSC task. After witnessing the experimental results, we briefly analyze the running time of the model. It should be note that currently in the TSC field, in order to make fair comparisons, FCN will be trained for 2000 times conventionally, since [PERSON] did so in the initial experiment. For other deep learning models, a fixed number of training periods are kept as well. We assume that NNE also follow this default rule. In our RSE-FCN, the feature picking and basic model training process of RSE-FCN is approximately equivalent to totally construct eight FCNs. In FCN, only a single model needs to be trained, while NNE integrated 60 models. According to the volume of these models, within the same hardware condition, the time consumption of RSE-FCN is about 6-10 times of that of single FCN, and will be 1/6-1/10 of that of NNE. Above comparison establish the significance of our proposed RSE-FCN. Furthermore, its mechanism is analyzed by the representative dataset Beef with balanced class and middle-level time steps. Instances in Beef have dimensionality of 470 and categories of 5. Taking N = 8 as an example, as given in Figure 5, the first image in the upper left corner is the complete curve of the five categories instances. Images from 2 to 9 show the 8 equally divided intervals and their evaluating accuracy. It can be seen from Figure 5 that in the second, seventh and eighth intervals, all curves mainly differ in amplitude. Furthermore, though there exists some fluctuations, compared with that in the others, they are tiny and irregular, which may be caused by noise and not category-related. Thus, in these intervals, all instances produce similar activation values  
Figure 5: Curves of time series instances of Beef dataset and the corresponding accuracy given by candidates evaluation model. when perform convolution operations with the filter matrix, making themselves to be indistinguishable, while in the rest intervals, differences appear in aspects of amplitude, change tendency, fluctuations, etc., thus they can be distinguished more easily and achieve higher accuracies.