# Software Operation Anomalies Diagnosis Method Based on a Multiple Time Windows Mixed Model
## 6 Conclusions  
In this study, we developed an anomaly detection integrated method based on the anomaly continuity criterion. Different from traditional combinatorial methods, this method considers the weighted statistics of each test instance in different anomaly time windows when selecting the optimal detector. Two detector-integrated methods based on accuracy and sensitivity are proposed. To verify its effectiveness, the proposed method is evaluated with seven real datasets and one real-time data collection.  
The results obtained from our proposed method demonstrate the successful integration of anomaly effects, aligning with our initial expectations. In certain instances, the integrated results exhibit a high level of consistency with the outcomes obtained from the optimal detector. In the majority of cases, there is a marginal decrease of only 3% in precision, indicating a minimal impact on accuracy.  
Furthermore, our proposed multiple time window strategy is specifically designed to capture the temporal characteristics of cumulative anomalies. This approach allows for a more effective extraction of the distribution of anomaly data. By targeting these temporal aspects, our method enhances the ability to accurately identify and analyze cumulative anomalies.  
These findings highlight the efficacy of our approach in achieving consistent and accurate anomaly detection results. The integration of anomaly effects and the utilization of the multiple time window strategy contribute to a comprehensive understanding of the anomalies, leading to improved detection performance. The minimal decrease in precision further demonstrates the robustness and reliability of our proposed method.  
**Author Contributions:** Conceptualization, T.S., J.A. and Z.Z.; methodology, T.S., J.A. and Z.Z.; software, T.S. and Z.Z.; validation, T.S. and Z.Z.; investigation, Z.Z. and T.S.; resources, T.S.; data curation, T.S.; writing--original draft preparation, T.S. and Z.Z.; writing--review and editing, T.S., J.A. and Z.Z.; visualization, T.S.; supervision, J.A.; project administration, J.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The source code of the method implementation can be downloaded at ([[https://gitee.com/stlinger/anomaly-detection-Integrated-model](https://gitee.com/stlinger/anomaly-detection-Integrated-model)]([https://gitee.com/stlinger/anomaly-detection-Integrated-model](https://gitee.com/stlinger/anomaly-detection-Integrated-model)), accessed on 10 August 2023). The publicly available dataset used for the anomaly detection task can be found downloaded at [32,33] ([[https://github.com/waico/SkAB](https://github.com/waico/SkAB)]([https://github.com/waico/SkAB](https://github.com/waico/SkAB)), and [[https://github.com/numenta/NAB](https://github.com/numenta/NAB)]([https://github.com/numenta/NAB](https://github.com/numenta/NAB)), accessed on 1 October 2023).  
**Acknowledgments:** The authors would like to thank the editors and anonymous reviewers for their constructive comments and suggestions for improving this work.  
**Conflicts of Interest:** The authors declare no conflict of interest.