# CDSTAEP: Cross-Domain Spatial-Temporal Association Learning for Abnormal Events Prediction
## 5 Experiments and Evaluation
### Experimental Results
#### 5.2.3 CDSTAEP Model and Baseline Model  
This experiment aims to compare multiple forecasting methods and analyze the contribution of event relationships to the prediction of abnormal events. To better evaluate the proposed forecasting model CDSTAEP, we choose two mainstream forecasting models, LSTM-ATT and LSTM, as baseline methods for comparison testing, and compared the time efficiency. **LSTM:** It is a popular RNN recurrent neural network model in deep learning. Since it can selectively memorize long-term correlations, it is suitable for prediction tasks with time-dimensional characteristics. **LSTM-ATT:** It is an LSTM model with an attention mechanism in deep learning. Since it can capture the weights of critical parts of the data set, it can implement prediction tasks in the time dimension in a targeted manner. **AUC score. ** After resampling the original data using the sliding window, we set the ratios of the training set to 0.2, 0.4, 0.6, and 0.8, respectively, and the rest as the test set. The prediction model CDSTAEP we proposed and the baseline methods LSTM-ATT and LSTM can predict time series data. Here, we mainly discuss the impact of adding cross-domain event relations on event prediction. Figure 8 and Table 7 record the comparison of the prediction effects of the three models under different training ratios. As shown in Figure 8 and Table 7, under different proportions of training sets, the AUC score of the models CDSTAEP, LSTM, and LSTM-ATT for predicting abnormal events are relatively similar, and they all reflect good robustness. The prediction performance of LSTM-ATT is better than that of LSTM; in contrast, the prediction performance of the CDSTAEP model is the best. Due to the large sparsity of abnormal events in the data set, when the training ratio is low, the prediction results may be reversed. For example, when the training ratio is 20%, the AUC score of each model is less than 0.5. When the training ratio is 40%, the AUC score of LSTM is also less than 0.5. As the proportion of the training set increases, the comprehensive prediction effect shows an increasing trend. The AUC \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Time(s)**} & \multicolumn{4}{c}{**AUC**} \\ \cline{3-6}  & & **Training Ratio** & **Training Ratio** & **Training Ratio** & **Training Ratio** \\ \hline / & / & **20** & **40** & **60** & **80** \\ CDSTAEP & 61 & 0.4981 & 0.5430 & 0.6685 & 0.7440 \\ LSTM-ATT & 67 & 0.4554 & 0.5093 & 0.6385 & 0.6987 \\ LSTM & 55 & 0.4431 & 0.4896 & 0.6133 & 0.6771 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Time efficiency and AUC for different classifiers on the dataset. Figure 8: Comparison of prediction effects of different models: the horizontal axis is the training ratio, and the vertical axis is the AUC score. score of the CDSTAEP model increases from 0.4981 to 0.7440, and the best prediction effect is 4.53% and 6.69% higher than that of LSTM-ATT and LSTM, respectively. The difference is significant. **ACC score. ** Similarly, we compared ACC scores on the same data set. As shown in Figure 9 and Table 8, under the same training set proportion, the CDSTAEP model still obtains the highest scoring result compared with the LSTM-ATT and LSTM baseline models, similar to the AUC scoring results. Generally, in the existing network data set with no obvious abnormal features, the models using attention mechanism have achieved relatively good results. The accuracy of CDSTAEP model is 5.5% and 11.5% higher than LSTM-ATT and LSTM, respectively. **Time efficiency. ** In the actual application scenarios, the detection and prediction of abnormal events, the time efficiency of the classifier is an important factor in evaluating the model.