# A New Method of Time-Series Event Prediction Based on Sequence Labeling
## 3 Experiment
### Performance Comparison and Discussion  
#### 3.4.1 Performance Comparison  
In this section, the performance of CX-LC and other models were compared. Because the ratio of positive and negative samples was not balanced, Recallratio, Precision \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Hyper-Parameter** & **djia30** & **Web** & **PM2.5** & **CO** & **Temp** \\ \hline \#(samples)—N & 30 & 50,000 & 38 & 24 & 100 \\ \#(subsequences)—K & 518 (wk) & 26 (mth) & 130 (wk) & 111 (day) & 222 (wk) \\ subsequence length—L & 5 (day) & 30 (day) & 7 (day) & 24 (h) & 7 (day) \\ observation dimension—D & 4 & 2 & 1 & 1 & 1 \\ history length—\(\tau\) & 50 (wk) & 12 (mth) & 20 (wk) & 20 (day) & 50 (wk) \\ positive ratio (\%) & 19.5 & 28.2 & 32.1 & 37.0 & 38.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Data set statistics. ratio, and F1-score were used as evaluation indicators at the same time. All reports were the average results of five repeated experiments and were run in the same environment. Table 2 shows the prediction results. By comparing the results of labeling and classification prediction methods, Q1 can be answered. The results in Table 2 show that, in addition to the Webtraffic and Djia30 data sets, the labeling prediction results of the other three data sets are basically better than the classification prediction results, converting the event prediction problem into a sequence labeling problem can greatly improve the prediction effect. There is still valuable information transfer between the subsequence sets, and the labeling prediction method predicts the events of the subsequence sets in chronological order; the prediction at each moment is related to the prediction at the previous moment. The algorithmic process of event prediction on the subsequence set, in turn, can well capture the important correlations in the time propagation process. Comparing the classification and prediction methods, EvoNet and Time2 Graph has a better classification effect than the sequential model X-HMM. Time2 Graph models the aggregated static graph, and the EvoNet model performs node-graph interaction during the time graph propagation, making it more suitable for the time modeling of an evolutionary state graph. This indicates that, in future research, we can try to build a graph model to capture the characteristic information of the subsequence, and then carry out label prediction. Vertical comparison of classification results shows that the Precisionratio of each model is higher than the Recallratio, except for the Djia30 data set, which indicates that the model has a low misjudgment rate for positive events, but it can not comprehensively detect positive events. By comparing the results of the four labeling prediction methods, Q2 can be answered. The results in Table 2 show that, on all data sets, the prediction performance of the CX-LC model is better than the other three models. Comparing CX-LC and X-LC, the prediction performance of CX-LC is much higher than that of the X-LC model, which means that, when making labeling predictions, the prediction performance of the model is easily affected by input features. The original data of as input ignore the influence of each subsequence itself on the event, and the mode is the most direct response to the occurrence of the event. Expressing the subsequence as a weighted combination of each mode can effectively extract the important information in the subsequence. Comparing CX-LC and C-LC, it can be seen that the prediction performance of CX-LC is higher than that of the C-LC model, and feature extraction does improve the prediction accuracy, avoiding the influence of redundant information on the prediction model. Comparing CX-LC and CX-L, it can be seen that the prediction performance of CX-LC is slightly higher than that of the CX-L model, indicating that considering the transition relationship between events can effectively correct the prediction results of LSTM.