# Student Behavior Prediction of Mental Health Based on Two-Stream Informer Network
## 2 Related Work
### Time-Series Classification  
The recognition and prediction of students with mental health abnormalities can be regarded as a time-series classification problem. Time-series classification methods based on machine learning and deep learning are currently the mainstream time-series classification methods [22; 23].  
Some early methods adopted machine learning classifiers, and can achieve satisfactory results in some specific domains [22]. [PERSON] et al. [24] constructed a classifier, GDTW-P-SVMs, with variable-length input sequences; the DTW method was used to convert the native time series into feature vectors, which were then used as the input to the SVM model. [PERSON] et al. [25] proposed a split detection method for decision tree induction by exhaustively searching for the optimal time series in the data. [PERSON] et al. [26] proposed an early multivariate time-series classification method using a Gaussian process learning method to estimate the minimum length required by the time series to help construct an integrated classifier with expected accuracy.  
In recent years, methods based on deep learning have been widely used in various domains, including sequential classification tasks. [PERSON] et al. [27] proposed a Multilayer Perceptron (MLP), whose structure consists of multiple fully connected layers, each of which is followed by a ReLU activation function and finally a Softmax classifier. [PERSON] et al. [27] proposed a Fully Convolutional Network (FCN). Different from previous convolution, FCN uses Global Average Pooling (GAP) to replace the final full connection layer, which can not only reduce the number of parameters, but also improve the experimental accuracy. [PERSON] et al. [27] proposed a Residual Network (ResNet) to solve the problem of gradient disappearance in the process of deepening layers through residual links. [PERSON] et al. [28] proposed a Multi-scale Convolutional Neural Network (MCNN) which uses multiple branches for transformation to obtain features of different frequencies and time scales, and outputs them through two convolutional layers--a fully connected layer and finally a Softmax layer. [PERSON] et al. [29] proposed a Multi-Channel Deep Convolutional Neural Network (MCDCNN) that automatically learns the features of a single univariate time series in each channel, then combines the output of all channels into the feature representation of the last layer and finally connects it to the MLP layer for classification. [PERSON] et al. [30] modified the Time Warping Invariant Echo State Network (TWIESN) applied to time-series prediction and applied it to time-series classification. [PERSON] et al. [31] proposed the LSTM-FCN model, where the time series were respectively passed through LSTM and FCN, and then the data obtained from the two channels were splintered for the final classification prediction. [PERSON] et al. [32] introduced a Transformer model into time-series classification and built a two-tower structure to code time features and channel features, respectively. The model can visualize the features learned by the model, and has certain interpretability.