## 3 Taxonomy of MTSAD Methods
### Related Learning Paradigms  
Unsupervised, semi-supervised and self-supervised methods are common learning paradigms in MTSAD, and they can be distinguished according to the label information of the data and the degree of supervision: \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Model** & **Venue** & **Bonkbone** & **LP** & **S/T** & **T/F** & **Input** & **Code** & **Language** \\ \hline \hline \multicolumn{10}{c}{Forecasting} \\ \hline DeepAnt [22] & IEEE’2018 & CNN & Un & T & T & W & ✓ & Pytorch \\ TCN-ms [23] & IOP’2019 & CNN & Semi & T & T & W & – & – \\ TimesNet [24] & ICLR’2023 & CNN & Un & T & F & W & ✓ & Pytorch \\ LSTM-NDT [25] & KDD’2018 & RNN & Un & T & T & W & ✓ & Tensorflow \\ LGMAD [26] & Elsevier’2019 & RNN & Semi & T & T & P & – & – \\ THOC [27] & NeurIPS’2020 & RNN & Self & T & T & W & – & – \\ AD-LTI [28] & TKDE’2020 & RNN & Un & T & F & P & – & – \\ MTAD-GAT [29] & ICDM’2020 & GNN & Self & T & W & ✓ & Tensorflow \\ GDN [30] & AAAI’2021 & GNN & Un & S & T & W & ✓ & Pytorch \\ FuSAGNet [31] & KDD’2022 & GNN & Un & ST & T & W & – & – \\ DVGCRN [32] & ICML’2022 & GNN & Un & ST & T & W & – & – \\ DyGraphAD [33] & ACM’2024 & GNN & Un & ST & T & W & – & – \\ MAD [34] & IJCNN’2022 & Transformer & Self & T & T & W & – & – \\ CLFormer [35] & EAAI’2023 & Transformer & Un & ST & T & W & – & – \\ AnomalyBERT [36] & ICLR’2023 & Transformer & Self & T & T & W & ✓ & Pytorch \\ \hline \multicolumn{10}{c}{Reconstruction} \\ \hline DAGMM [37] & ICLR’2018 & AE & Un & T & T & P & ✓ & Pytorch \\ MSCRED [38] & AAAI’2019 & AE & Semi & T & T & W & ✓ & Tensorflow \\ USAD [39] & KDD’2020 & AE,GAN & Un & T & T & W & ✓ & Pytorch \\ NPSR [40] & NeurIPS’2024 & AE & Un & T & T & W & ✓ & Pytorch \\ LSTM-VAE [41] & IEEE’2018 & VAE,RNN & Semi & T & T & P & ✓ & Tensorflow \\ OmniAnomaly [42] & KDD’2019 & VAE,GRU & Semi & T & T & W & ✓ & Tensorflow \\ InterFusion [43] & KDD’2021 & VAE & Un & T & T & W & ✓ & Tensorflow \\ SLA-VAE [44] & WWW’2022 & VAE & Semi & T & T & W & ✓ & Pytorch \\ LARA [16] & WWW’2024 & VAE & Un & T & T & W & ✓ & Pytorch \\ MAD-GAN [45] & ICANN’2019 & GAN & Un & ST & T & W & ✓ & Tensorflow \\ TadGAN [46] & IEEE’2020 & GAN,LSTM & Un & T & T & W & ✓ & Pytorch \\ MIM-GAN [47] & IEEE’2023 & GAN,LSTM & Un & T & T & W & ✓ & Tensorflow \\ DAEMON [48] & WSDM’2023 & GAN,AE & Un & T & W & ✓ & [PERSON] \\ DCGAN [17] & AAAI’2024 & GAN,CNN & Un & T & T & W & ✓ & [PERSON] \\ DiffusionAE [49] & ICDMW’2023 & Diffusion,AE & Un & T & T & W & ✓ & [PERSON] \\ \(D^{3}R\)[18] & NeurIPS’2024 & Diffusion & Un & ST & F & W & ✓ & [PERSON] \\ TransAnomaly [50] & CCDC’2021 & Transformer,VAE & Un & T & T & W & ✓ & [PERSON] \\ Anomaly Transformer [51] & ICLR’2022 & Transformer & Un & T & T & W & ✓ & [PERSON] \\ TranAD [52] & VLDB’2022 & Transformer & Un & T & T & W & ✓ & [PERSON] \\ MEMTO [53] & NeurIPS’2023 & Transformer & Un & T & T & W & ✓ & [PERSON] \\ Dual-TF [54] & WWW’2024 & Transformer & Un & T & TF & W & ✓ & [PERSON] \\ CATCH [55] & arXiv’2024 & Transformer & Un & T & TF & W & ✓ & [PERSON] \\ \hline \multicolumn{10}{c}{Contrastive} \\ \hline AnomalyLLM [56] & arXiv’2024 & LLMs & Un & ST & T & W & ✓ & [PERSON] \\ aLLM4 TS [57] & ICML’2024 & LLMs & Self & T & T & W & ✓ & [PERSON] \\ AnomalyLLM [58] & IJCAI’2024 & LLMs & Self & T & T & W & – & – \\ PatchAD [59] & arXiv’2024 & MLP-Mixer & Un & T & T & W & ✓ & [PERSON] \\ TRL-CFC [60] & Elsevier’2022 & Transformer & Un & T & T & W & – & – \\ RESIST [61] & Springer’2022 & Transformer & Un & T & T & W & – & – \\ Dedetector [13] & KDD’2023 & Transformer & Self & T & T & W & ✓ & Pytorch \\ SimAD [62] & arXiv’2024 & Transformer & Un & T & T & W & ✓ & Pytorch \\ RESTAD [63] & arXiv’2024 & Transformer & Un & T & T & W & ✓ & Pytorch \\ \hline \end{tabular}
\end{table} 
Table 1: Multivariate Deep Anomaly Detection Models in Time Series.