# Student Behavior Prediction of Mental Health Based on Two-Stream Informer Network
## 4 Experimental Results and Analysis
### Comparison with Representative Works  
#### 4.2.1 Accuracy Comparison  
We evaluated the performance of the proposed TSIN model for timing classification tasks and proved the effectiveness of feature engineering based on the two-stream informer network.  
The experimental results are shown in Table 2. The results were calculated using the TSIN model in the AUSLAN, CharacterTrajectories, Libras and Wafer data sets for the 10 datasets of MTS. Compared with the best results of the other 10 baselines, the results were improved by 0.50%, 0.20%, 0.55% and 0.55% in these datasets, respectively. The TSIN model had the best accuracy of 81.45% in the STU dataset, an improvement of 0.15% compared with the other best results. FCN had the highest accuracy of 98.77% for ArabicDigits and 91.84% for UWave out of 11 datasets; GTN with the same two-stream structure had the best accuracy of 98.38% on the JapaneseVowels dataset. On the CMUsubject16 dataset, both the TSIN model and the GTN model with the same two-stream structure achieved perfect accuracy.  
In addition, TCN, ResNet and Encoder also had high accuracy, and TCN and the TSIN had the highest accuracy of 87.00% on the ECG data set. At the same time, TCN, ResNet, Encoder, FCN and GTN also had the highest precision of 100.00% on the WalkvsRun data set alongside the TSIN. The success of ResNet is largely due to its deeply flexible architecture; the small number of filters in TCN is the main reason for its success on small datasets,  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & **MLP** & **FCN** & **ResNet** & **Encoder** & **MCNN** & **t-LeNet** & **MCDCNN** & **TCN** & **TWIESN** & **GTN** & **TSIN** \\ \hline AUSLAN & 93.09 & 93.54 & 92.91 & 67.58 & 1.05 & 1.05 & 80.35 & 90.60 & 14.88 & 92.70 & **94.04** \\ ArabicDigits & 95.82 & **98.77** & 98.55 & 96.36 & 10.02 & 10.02 & 95.9  
but this shallow architecture cannot capture the variability of large time-series datasets efficiently modeled by the FCN and ResNet architectures. MLP also had good performance in multiple datasets because it contains enough connection layers to fit real-time trends.  
The reason that MCDCNN did not have the highest accuracy on any dataset may be due to the use of a nonlinear FC layer instead of the GAP in FCN and ResNet--this FC layer reduces the effect of learning time-invariant features. Meanwhile, for the only TWIESN model in the experiment that uses a cyclic structure, the accuracy was poor in time series with longer time sequences, such as the AUSLAN, CMUsubject16, UWave and WalkvsRun datasets, and their predictions were all far from being able to compete with the best accuracy because the cyclic structure may lose some of the information that appears early in long time-series information.  
The accuracy of the MCNN and t-LeNet architectures was very low, and they had the worst prediction accuracy of the eight datasets; both of these models augment the training data by extracting subsequences. Unlike models that classify from the entire subsequence, they learn features from shorter subsequences and finally assign labels to the time series using majority voting. The lower accuracy of these two methods suggests that this method of extracting subsequences from time series does not guarantee that features of the time series are not lost when they are segmented.  
Overall, the results show that the proposed model TSIN was the best architecture with the highest accuracy achieved on the eight evaluated datasets, followed by FCN and GTN, both of which had the highest accuracy on three datasets. MCNN and t-LeNet, which partition the time series into subseries, had the worst prediction results.