# A New Method of Time-Series Event Prediction Based on Sequence Labeling
## 3 Experiment
### Performance Comparison and Discussion
#### 3.4.2 Discussion  
It is not difficult to find that CX-LC is sometimes better than EvoNet, sometimes worse, but sometimes very similar. In order to further discuss the predictive performance of the two models and analyze their classification ability for different samples, this article discussed the misclassification samples of the two models using PM2.5 data and Djia30 data. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multirow{2}{*}{**Datasets**} & \multicolumn{2}{c}{**Dji\_a30**} & \multicolumn{2}{c}{**Web**} & \multicolumn{2}{c}{**PM2.5**} & \multicolumn{2}{c}{**CO**} & \multicolumn{2}{c}{**Temp**} \\ \cline{2-13}  & **P** & **R** & **F1** & **P** &  
For the prediction task of PM2.5, among the negative samples predicted by CX-LC as positive samples, the top 20 samples with the highest frequency of occurrence are shown in Figure 6. According to this figure, each historical data point used as a prediction sample contains 140 days (20 weeks) of PM2.5 data, with significant fluctuations in the overall sample. Regardless of which stage of the data, there is a high weekly mean. When CX-LC predicts such historical data, it is easy to predict negative samples as positive samples. According to the definition in Section 3.1, positive samples mean a high level of PM2.5. Under the long-term high PM2.5 indicators, relevant departments may control the air, so as to quickly reduce the PM2.5 value in a short time. In this case, the model CX-LC often does not have good strain energy, and how to better predict this sudden change is the focus of future research. Similarly, for the PM2.5 dataset, among the positive samples predicted by CX-LC as negative samples, the top 20 samples with the highest frequency of occurrence are shown in Figure 7. Observing these 20 samples, it can be found that the 140 day long historical data only have significant fluctuations in the middle stage (such as the 19 th, 26 th, 57 th, 101 st week, etc. ), or is generally relatively flat, with periodic fluctuations and no obvious trend (such as the 171 st, 180 th, 182 nd, 632 nd week, etc.). CX-LC has a poor prediction performance for samples with stable fluctuations and low mean values. Compared to predicting negative cases as positive cases, CX-LC is less likely to predict positive cases as negative cases; because the increase in PM2.5 value often takes a certain amount of time, it is rare for the air to suddenly deteriorate, such as an increase in vehicles traveling during holidays and an increase in vehicle exhaust emissions. As shown in Table 2, as a classification model, EvoNet also has a good classification performance on the PM2.5 dataset. To compare with CX-LC, Figures 8 and 9 show EvoNet's two types of classification errors. considers the more remote dependencies of data, when predicting each sample, it not only considers the sample itself, but also considers all previous data. For samples with no obvious or slow trends, CX-LC can make correct predictions based on earlier history. For small-scale datasets, CX-LC can often achieve better prediction results, but for large-scale datasets, EvoNet has a better prediction performance. To analyze CX-LC's shortcomings, this article uses the djia30 dataset to discuss sample features. As shown in Table 2, when predicting the djia30 dataset, EvoNet has a higher Precisionratio than CX-LC. Based on this result, the classification performance of the two models for positive cases is discussed. As shown in Figure 10, there are 40 typical positive samples in the djia30 dataset that were misclassified by CX-LC but correctly classified by EvoNet. These samples were misclassified as negative by CX-LC and correctly classified as positive by EvoNet. As shown in the figure, the weekly historical data of this type of sample have significant fluctuations.