# Unsupervised Deep Anomaly Detection for Industrial Multivariate Time Series Data
## 3 Materials and Methods
### Inference
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Size**} & \multicolumn{8}{c}{**AUC**} & \multicolumn{8}{c}{**F1**} \\ \cline{2-13}  & **MSDS** & **MBA** & **SMAP** & **SWaT** & **WADI** & **SMD** & **MSDS** & **MBA** & **SMAP** & **SWaT** & **WADI** & **SMD** \\ \hline
4 & 0.8129 & 0.9980 & 0.8373 & 0.8383 & 0.9121 & 0.9987 & 0.9318 & 0.9961 & 0.7869 & 0.8066 & 0.8082 & 0.9960 \\
8 & 0.9609 & 0.9824 & 0.9940 & 0.8423 & 0.9090 & 0.9983 & 0.9658 & 0.9733 & 0.9420 & 0.8047 & 0.7156 & 0.9956 \\
12 & 0.9004 & 0.9570 & 0.9934 & 0.8409 & 0.9064 & 0.9979 & 0.8895 & 0.9333 & 0.9361 & 0.7960 & 0.6554 & 0.9806 \\
16 & 0.9004 & 0.9378 & 0.9924 & 0.8387 & 0.9000 & 0.9977 & 0.8895 & 0.9056 & 0.9274 & 0.7826 & 0.5399 & 0.9787 \\
20 & 0.9003 & 0.9070 & 0.6973 & 0.8391 & 0.8882 & 0.9972 & 0.8894 & 0.8530 & 0.5180 & 0.7847 & 0.4071 & 0.9742 \\
24 & 0.9003 & 0.8606 & 0.6962 & 0.8359 & 0.8703 & 0.9975 & 0.8894 & 0.7879 & 0.5111 & 0.7657 & 0.2969 & 0.9767 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Impact of window size parameter on performance. **Funding:** This research was funded by the National Key Research and Development Program of China, grant number 2022 YFB3305300, and supported by State Grid Shandong Electric Power Company Technology Project (No. 520627230004). The authors declare that this study received funding from State Grid Shandong Electric Power Company. The funder had the following involvement with the study: data curation and validation. **Data Availability Statement:** Publicly available datasets were analyzed in this study. This data can be found here: [[https://github.com/imperial-qore/TranAD/tree/main/data](https://github.com/imperial-qore/TranAD/tree/main/data)]([https://github.com/imperial-qore/TranAD/tree/main/data](https://github.com/imperial-qore/TranAD/tree/main/data)) (accessed on 16 December 2023). Restrictions apply to the availability of WADI dataset. Data was obtained from iTRUST and is available at [[https://itrust.sutd.edu.sg/itrust-labs_datasets/dataset_info/#wadi](https://itrust.sutd.edu.sg/itrust-labs_datasets/dataset_info/#wadi)]([https://itrust.sutd.edu.sg/itrust-labs_datasets/dataset_info/#wadi](https://itrust.sutd.edu.sg/itrust-labs_datasets/dataset_info/#wadi)) (accessed on 16 October 2023) with the permission of iTRUST. **Conflicts of Interest:** Authors [PERSON] and [PERSON] were employed by Information and Telecommunication Company, State Grid Shandong Electric Power Company. The remaining authors declare that the research was conducted in the absence of any commercial or financial rela-tionships that could be construed as a potential conflict of interest. The authors declare that this study received funding from State Grid Shandong Electric Power Company. The funder had the following involvement with the study: data curation and validation. ## References

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