# Software Operation Anomalies Diagnosis Method Based on a Multiple Time Windows Mixed Model
## 5 Result and Discussion
### Impact of Different Time Windows on Model Selection
The results obtained demonstrate a certain degree of reproducibility, as indicated by the variability of anomalies observed in some of the detectors shown in the figure. The majority of the detectors employed are traditional machine learning algorithms, while neural network algorithms like VAE and AutoEncoder exhibit notably consistent anomaly detection outcomes. Figure 16: Distribution of anomalies with a time window of 60 for cardio dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Size of Time Windows** & **Detector of Choice** & **Time Widow Combination** & **Detector of Choice** \\ \hline
2 & AutoEncoder, CBLOF, KNN, PCA & 2-10 & AutoEncoder, CBLOF, KNN, PCA \\
10 & OCSVM, VAE, AutoEncoder, CBLOF, KNN, PCA & 2-20 & VAE, AutoEncoder, PCA \\
20 & VAE, AutoEncoder, PCA & 2-30 & VAE, AutoEncoder, PCA \\
30 & VAE, AutoEncoder, PCA & 2-40 & VAE, AutoEncoder, PCA \\
40 & OCSVM, POREST, VAE, AutoEncoder, PCA & 2-50 & VAE, AutoEncoder, PCA \\
50 & VAE, AutoEncoder, CBLOF, KNN, PCA & 2-60 & AutoEncoder, CBLOF, KNN, PCA \\
60 & OCSVM, VAE, AutoEncoder, CBLOF, KNN, PCA & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Algorithm selection results under different time windows for cardio dataset. Table 11 provides clear evidence that the proposed method accurately selects three models with the highest anomaly detection performance in the SKAB dataset: PCA, AutoEncoder, and PCA (repeated entry). The integration results of these three models demonstrate optimal performance or a mere 0.1% degradation in each metric, as mentioned. However, the performance of the proposed methods varies across the NAB datasets. Exceptional anomaly detectors were identified by our method on the ad-cpc, cardio, and vowels datasets. Additionally, our two integrated models successfully synthesized abnormal results from multiple models, resulting in improved performance compared to the basic detector. Notably, AUC, precision, recall, and F1 scores exhibited a 1-2% improvement across the different datasets. Nevertheless, there are also some perplexing results. In the case of the cardio dataset, VAE, AutoEncoder, and PCA exhibited identical performance, which is puzzling. However, since these three algorithms were selected by the optimization model, the results of the proposed hybrid algorithm align with those of the other algorithms. Regarding the occu and speed datasets, our method yielded only a few outstanding results. Observational indicators reveal that our method falls short of optimal performance in terms of AUC and recall. A detailed comparison of the performance of selected base detectors indicates that certain methods achieved 100% effectiveness on these datasets. However, this poses a challenge to the accuracy of the integration results, as the integration model takes into account negative outcomes from other models. Simultaneously, the high recall rate significantly impairs accuracy and F1 values. It is plausible that the choice of an anomaly threshold affects the range of identified anomalies within the data. Further research is needed to determine a reasonable threshold selection method. In contrast, the performance metrics on the twitter dataset were the poorest. Upon analyzing the data, we discovered that the abnormal changes within the twitter dataset were abrupt, resulting in the excessive inclusion of normal data across numerous time windows. We believe that, in practical applications, the choice of the time window should be correlated with the number of detectable anomalies. This aspect requires further experimentation and investigation. Based on this experiment, we can derive the following conclusions. Our two integration methods are designed to harmonize the anomaly results obtained from different detectors. The final results exhibit an error margin of approximately plus or minus 3% from the optimal value of the detector. Currently, we do not have a clear understanding of the decisive factors that contribute to better or worse results. At present, it appears to be a random issue.