# ChaMTeC: CHAnnel Mixing and Temporal Convolution Network for Time-Series Anomaly Detection
## 5 Experimental Results and Performance Evaluation
### Performance Comparison  
Tables 6 and 7 present comprehensive experimental results comparing ChaMTeC with state-of-the-art models across multiple benchmark datasets. The evaluation metrics include the Point-wise Precision (\(P_{CPA}\)), Recall (\(R_{CPA}\)), and F1-score (\(F_{1 CPA}\)), which are widely used in anomaly detection tasks. Our proposed ChaMTeC model demonstrates superior overall performance, achieving the highest average F1-CPA score of 0.4053 without attention and 0.3121 with attention across all public benchmark datasets. This significantly outperforms recent state-of-the-art approaches, namely, LMSAutoTSF (0.2244), iTransformer (0.3488), TimeMixer (0.3910), PatchTST (0.2444), and AnomalyTransformer (0.2742). The performance advantage is particularly evident in the precision and recall metrics, where ChaMTeC achieves balanced scores of \(P_{CPA}=0.4604\) and \(R_{CPA}=0.4447\), indicating its robust detection capability without sacrificing accuracy.  
On individual datasets, ChaMTeC shows exceptional performance on the SWAT dataset, achieving an F1-CPA score of 0.7270 and 0.2629 without and with attention, respectively. This is comparable to TimeMixer (0.7497), and it substantially outperforms LMSAutoTSF (0.0845), PatchTST (0.1092), and AnomalyTransformer (0.1006). The strong performance on SWAT, a complex industrial control system dataset, demonstrates our model's effectiveness in real-world scenarios. Similarly, on our newly introduced WaterLog dataset, ChaMTeC achieves an F1-CPA of 0.3861 and 0.4635, consistently outperforming most baseline models (LMSAutoTSF: 0.1915, iTransformer: 0.3284, TimeMixer: 0.3309, PatchTST: 0.2405). Our experimental results demonstrate that while AnomalyTransformer achieves superior performance on the Waterlog dataset when combined with our proposed dynamic thresholding approach (as detailed in Table 4), our method consistently outperforms all baseline approaches across the majority of the evaluated datasets. This comprehensive evaluation highlights the robustness and generalizability of our proposed framework. On the other hand, the analysis reveals an important nuance: while temporal attention mechanisms significantly improve detection accuracy for the Waterlog dataset, they prove less effective for the other datasets. This dataset-dependent performance variation suggests that our temporal attention mechanisms require more sophisticated anomaly-specific adaptations, particularly in handling diverse anomaly duration scales, varying temporal dependencies across different sensor types, and complex multi-variate interaction patterns.  
These comprehensive results validate that our proposed architecture's key components--inverted embedding, temporal encoding, and the MSE-based feedback mechanism with dynamic thresholding--work synergistically to enhance anomaly detection performance across diverse datasets. The consistent superior performance, particularly on industrial datasets such as SWAT and WaterLog, demonstrates ChaMTeC's practical utility in real-world applications.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Metric**} & **ChaMTeC** & **ChaMTeC** & **LMS** & **AutoTSF** & **iTransformer** & **TimeMixer** & **PatchTST** & **Anomaly** \\  & **w/o Attention** & & **(Ours)** & **AutoTSF** & **iTransformer** & **TimeMixer** & **PatchTST** & **Anomaly** \\ \hline \(R_{CM}\) & 0.3534 & 0.4312 & 0.1417 & 0.2785 & 0.2632 & 0.1743 & 0.4797 \\ \(R_{CPA}\) & 0.4253 & 0.5011 & 0.2954 & 0.4000 & 0.4452 & 0.3880 & 0.7866 \\ \(F_{1 CPA}\) & 0.3861 & 0.4635 & 0.1915 & 0.3284 & 0.3309 & 0.2405 & 0.5969 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Anomaly detection performance on the WaterLog\({}^{*}\) dataset (threshold with sliding window: thFactor = 2.0). The values highlighted in red and blue indicate the best and second best scores for each metric.