Table 2 Advantages and disadvantages of several commonly used sequence prediction methods

SVM

Possessing good generalization ability and being insensitive to noisy data [44].

difficulty occurs in selecting suitable kernel [45].

KNN

The principle is simple and there are few influencing factors [46].

The computational complexity is high, and the algorithm performance is easily affected by the samples [17].

ANN

Able to learn non-linear features of data [47].

Easily trapped in local optima [48].

LSTM

Has the ability to capture long-term sequence features [49].

difficult to balance the depth and operation time complexity of its model [50].

CNN-LSTM

Reducing the risk of overfitting can uncover more feature information [51, 52].

There is a problem of neural network model degradation [53].

GCN

High efficiency, possessing the ability to effectively learn data relationships, and providing interpretability [54].

There is a problem of excessive smoothing [55].