# ChaMTeC: CHAnnel Mixing and Temporal Convolution Network for Time-Series Anomaly Detection
## 6 Conclusions
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Metric**} & \multicolumn{2}{c}{**ChaMTeC**} & \multicolumn{2}{c}{**ChaMTeC**} & \multicolumn{2}{c}{**LMS**} & \multirow{2}{*}{**iTransformer**} & \multirow{2}{*}{**TimeMixer**} & \multirow{2}{*}{**PatchTST**} & \multicolumn{2}{c}{**Anomaly**} \\  & & & **(Ours)** & & & & & & **Trans** \\  & & **w/o Attention** & & & & & & & \\ \hline \multirow{2}{*}{MSL} & \(R_{\text{CPA}}\) & 0.3225 & 0.2901 & 0.3100 & 0.2725 & 0.3168 & 0.2761 & 0.2587 \\  & \(R_{\text{CPA}}\) & 0.1299 & 0.1118 & 0.1688 & 0.1120 & 0.1208 & 0.1004 & 0.1006 \\  & \(R_{\text{CPA}}\) & 0.1852 & 0.1614 & 0.2186 & 0.1588 & 0.1749 & 0.1472 & 0.1449 \\ \hline \multirow{2}{*}{PSM} & \(R_{\text{CPA}}\) & 0.5186 & 0.5245 & 0.4320 & 0.4942 & 0.5484 & 0.4681 & 0.5089 \\  & \(R_{\text{CPA}}\) & 0.1018 & 0.1084 & 0.0824 & 0.0913 & 0.1333 & 0.0907 & 0.0876 \\  & \(R_{\text{CPA}}\) & 0.1702 & 0.1797 & 0.1384 & 0.1541 & 0.2145 & 0.1520 & 0.1495 \\ \hline \multirow{2}{*}{SMAP} & \(R_{\text{CPA}}\) & 0.4369 & 0.5497 & 0.5034 & 0.3657 & 0.4283 & 0.4899 & 0.6634 \\  & \(R_{\text{CPA}}\) & 0.6392 & 0.5424 & 0.2254 & 0.2845 & 0.3892 & 0.3440 & 0.5294 \\  & \(R_{\text{CPA}}\) & 0.5190 & 0.5460 & 0.3113 & 0.3165 & 0.4078 & 0.4042 & 0.5889 \\ \hline \multirow{2}{*}{SMD} & \(R_{\text{CPA}}\) & 0.3341 & 0.3237 & 0.3043 & 0.3156 & 0.3287 & 0.3313 & 0.2959 \\  & \(R_{\text{CPA}}\) & 0.5842 & 0.5603 & 0.4668 & 0.5327 & 0.5582 & 0.5352 & 0.5059 \\  & \(R_{\text{CPA}}\) & 0.4251 & 0.4103 & 0.3691 & 0.3963 & 0.4081 & 0.4092 & 0.3871 \\ \hline \multirow{2}{*}{SWAT} & \(R_{\text{CPA}}\) & 0.6897 & 0.3629 & 0.1615 & 0.6926 & 0.6957 & 0.1951 & 0.2155 \\  & \(R_{\text{CPA}}\) & 0.7685 & 0.2061 & 0.0572 & 0.7462 & 0.8129 & 0.0758 & 0.0656 \\  & \(R_{\text{CPA}}\) & 0.7270 & 0.2629 & 0.0845 & 0.7184 & 0.7497 & 0.1092 & 0.1006 \\ \hline \multirow{2}{*}{Average} & \(R_{\text{CPA}}\) & 0.4604 & 0.4102 & 0.3422 & 0.4263 & 0.4636 & 0.3521 & 0.3885 \\  & \(R_{\text{CPA}}\) & 0.4447 & 0.3058 & 0.2005 & 0.3533 & 0.3989 & 0.2292 & 0.2685 \\  & \(R_{\text{CPA}}\) & 0.4453 & 0.3121 & 0.2244 & 0.3488 & 0.3910 & 0.2444 & 0.2742 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Anomaly detection performance (threshold with sliding window: thFactor = 2.0). The values highlighted in red and blue indicate the best and second best average scores for each metric. attention mechanisms enhance performance on WaterLog by capturing long-range dependencies, they prove less effective for datasets such as SWAT, where anomalies are more localized. This suggests that standard attention mechanisms may not always align with diverse anomaly types. Future improvements could incorporate sparse attention, dynamic feature gating, and hierarchical temporal modeling to enhance adaptability, ensuring robust detection across various real-world industrial settings. A significant contribution of this work is the introduction of the WaterLog dataset, which we have made publicly available to the research community. Unlike existing datasets, WaterLog was specifically restructured to reflect real-world industrial control system environments, where anomalies are rare events. By reducing the anomaly rate and restructuring the data distribution, we provide a more realistic benchmark for evaluating anomaly detection systems in industrial settings. This addresses a critical gap in existing benchmarks, which often fail to capture the true challenges of real-time anomaly detection in operational environments. Conceptualization and methodology, I.D., D.B. and M.B. ; software and validation, I.D. ; formal analysis, I.D., D.B. and M.B. ; investigation, I.D., D.B. and M.B. ; resources, I.D. ; data curation, D.B. and M.B. ; writing--original draft preparation, I.D., D.B. and M.B. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable.