# 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
However, based on the results, our multiple time window strategy proves effective in selecting anomaly detectors that yield similar recognition outcomes. Additionally, our integration method successfully combines these detectors to produce outstanding anomaly results. The results produced with the SKAB, cardio, and vowels datasets are visualized in Figure 17a which shows the visualization results of SKAB abnormal data, while Figure 17b visualizes the abnormal results of cardio, and Figure 17c visualizes the abnormal results \begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline
**Cardio** & **mixModel-pre** & **mixModel-sen** & **HBOS** & **1 of** & **OCSVM** & **iforest** & **VAE** & **AutoEncoder** & **CRLOF** & **COF** & **KNN** & **PCA** \\ \hline AUC & 0.9500 & **0.9500** & 0.8377 & 0.5968 & 0.9352 & 0.9163 & **0.9500** & **0.9500** & 0.8320 & 0.5062 & 0.8661 & **0.9500** \\ precision & **0.6011** & **0.6011** & 0.4699 & 0.2596 & 0.5027 & 0.4590 & **0.6011** & **0.6011** & 0.5355 & 0.1421 & 0.3500 & **0.6011** \\ recall & **0.6250** & **0.6250** & 0.4886 & 0.1932 & 0.5227 & 0.4773 & **0.6250** & **0.6250** & 0.5568 & 0.1477 & 0.2784 & **0.6250** \\ f1 & **0.6128** & **0.6128** & 0.4791 & 0.2215 & 0.5125 & 0.4680 & 0.6128 & **0.6128** & 0.5460 & 0.1448 & 0.3101 & **0.6128** \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Performance of each algorithm with the cardio dataset. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline
**Vowels** & **mixModel-pre** & **mixModel-sen** & **HBOS** & **1 of** & **OCSVM** & **iforest** & **VAE** & **AutoEncoder** & **CRLOF** & **COF** & **KNN** & **PCA** \\ \hline AUC & 0.9589 & **0.9683** & 0.6766 & 0.9434 & 0.7783 & 0.7484 & 0.6217 & 0.6217 & 0.9471 & 0.7749 & 0.9768 & 0.6062 \\ precision & 0.2586 & **0.4408** & 0.0822 & 0.2913 & 0.1438 & 0.1233 & 0.0959 & 0.  
of vowels. The dimensionality reduction operation of the dataset is primarily performed by the t-SNE method, and the dataset is projected onto a two-dimensional space. In the figure, the detection of abnormal points with 10 basic detectors and the 2 hybrid models proposed in this paper are repeated with points of different colors and sizes. Because other datasets are one-dimensional, and most abnormal points are outliers, no additional analysis is performed. The visualizations of cardio and vowels show that mixed models are more advantageous in detecting local outliers clustered together. When the data have locality, the method in this paper produces better classification when recognizing outliers. However, when the data are scattered or there are multiple aggregation points, this method achieves a good recognition rate for peripheral points but fails sometimes with local points. Concurrently, the application effect of this method with the SKAB dataset is inferior to that of the other two datasets. Based on these visualizations, the proposed hybrid model can be deemed useful when the characteristics of the data appear to be aggregated.