## 5 Conclusions  
We propose an anomaly explanation model based on adversarial convolution, which can perform anomaly detection and anomaly explanation on time series data of the cloud  
Figure 4: Performance comparison between dynamic threshold and dynamic threshold (no dynamic threshold) datasets.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\alpha\) & \(\beta\) & **P** & **R** & **F1** \\ \hline
0 & 1 & 0.8713 & 0.9999 & 0.9312 \\
0.1 & 0.9 & 0.8730 & 0.9999 & 0.9322 \\
0.3 & 0.7 & 0.8696 & 0.9999 & 0.9302 \\
0.5 & 0.5 & 0.8478 & 0.9999 & 0.9176 \\
0.7 & 0.3 & 0.8494 & 0.9999 & 0.9186 \\
0.9 & 0.1 & 0.8494 & 0.9999 & 0.9186 \\
1 & 0 & 0.8462 & 0.9999 & 0.9167 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance comparison of different \(\alpha\) and \(\beta\) on P1 dataset.  
platform. Compared with the traditional linear change, its convolutional structure can share parameters, thereby significantly reducing the number of network parameters, which can effectively avoid overfitting. The adversarial-based training process enables the model to amplify \"slight\" anomalies, enhancing the model's ability to identify anomalies that tend to be normal data. At the same time, the sensitivity setting of the adversarial training also makes the model applicable to a broader range of environments. Furthermore, POT optimizes detection intensively by modeling the tail of all values above a threshold. We also use loss function calculation for a simple attribution in the anomaly explanation part. We use seven data subsets of four public datasets to evaluate the model. The effect of the model also proves the effectiveness of the model type. Specifically, CAT-IADEF ranks first among all compared models, making CAT-IADEF a good choice for cloud platform anomaly detection.  
In the future, we will consider adding an attention mechanism, such as a multi-head attention mechanism, to the overall model to allow the model to focus on some additional weird sequence points. We also want to be able to add residual connections to the convolutional network to speed up the model. Finally, we consider studying the cost and environmental analysis in the actual deployment process of the model and try to make it achieve low energy consumption and pollution.  
Conceptualization, P.W. and P.C.; Methodology, P.W., P.C. and Z.Y.; Software, P.W.; Validation, Z.Y. and S.Q.; Investigation, J.C. and S.Q.; Writing-Original Draft Preparation, P.W. and Z.Y.; Writing-Review and Editing, P.C. and L.W.; Visualization, P.W.; Supervision, P.C. and L.W.; Funding Acquisition, P.C. and J.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science and Technology Program of Sichuan Province under Grant No. 2021 JDR0222 and No. 2020 YFG0326, the Talent Program of Xihua University under Grant No. Z202047 and No. Z222001, and the Municipal Government of Quzhou under Grant No. 2021D007, Grant No. 2021D008, Grant No. 2021D015, and Grant No. 2021D018.  
Not applicable.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. This data can be found here: [available online: [[https://github.com/khundman/telemanom](https://github.com/khundman/telemanom)]([https://github.com/khundman/telemanom](https://github.com/khundman/telemanom)) (accessed on 7 April 2022); available online: [[https://itrust.sutd.edu.sg/itrust-labs_datasets/dataset_info/](https://itrust.sutd.edu.sg/itrust-labs_datasets/dataset_info/)]([https://itrust.sutd.edu.sg/itrust-labs_datasets/dataset_info/](https://itrust.sutd.edu.sg/itrust-labs_datasets/dataset_info/)) (accessed on 7 April 2022); available online: [[https://github.com/waico/SKAB](https://github.com/waico/SKAB)]([https://github.com/waico/SKAB](https://github.com/waico/SKAB)) (accessed on 4 July 2022)].  
The authors declare no conflict of interest.