{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T15:45:41Z","timestamp":1781279141749,"version":"3.54.1"},"publisher-location":"New York, NY, USA","reference-count":41,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T00:00:00Z","timestamp":1665964800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Natural Science Foundation of China","award":["61836007"],"award-info":[{"award-number":["61836007"]}]},{"name":"National Natural Science Foundation of China","award":["61832017"],"award-info":[{"award-number":["61832017"]}]},{"name":"Shenzhen Municipal Science and Technology R&D Funding Basic Research Program","award":["JCYJ20210324133607021"],"award-info":[{"award-number":["JCYJ20210324133607021"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61972069"],"award-info":[{"award-number":["61972069"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,10,17]]},"DOI":"10.1145\/3511808.3557386","type":"proceedings-article","created":{"date-parts":[[2022,10,16]],"date-time":"2022-10-16T01:22:22Z","timestamp":1665883342000},"page":"2230-2239","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["MARINA"],"prefix":"10.1145","author":[{"given":"Jiandong","family":"Xie","sequence":"first","affiliation":[{"name":"Huawei Cloud Database Innovation Lab, Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Cui","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology, Hong Kong, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feiteng","family":"Huang","sequence":"additional","affiliation":[{"name":"Huawei Cloud Database Innovation Lab, Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Liu","sequence":"additional","affiliation":[{"name":"Huawei Cloud Database Innovation Lab, Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Zheng","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China, Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,10,17]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Unsupervised real-time anomaly detection for streaming data. Neurocomputing 262 (Nov","author":"Ahmad Subutai","year":"2017","unstructured":"Subutai Ahmad , Alexander Lavin , Scott Purdy , and Zuha Agha . 2017. Unsupervised real-time anomaly detection for streaming data. Neurocomputing 262 (Nov . 2017 ), 134--147. Subutai Ahmad, Alexander Lavin, Scott Purdy, and Zuha Agha. 2017. Unsupervised real-time anomaly detection for streaming data. Neurocomputing 262 (Nov. 2017), 134--147."},{"key":"e_1_3_2_1_2_1","volume-title":"Asian Conference on Computer Vision. Springer, 622--637","author":"Akcay Samet","year":"2018","unstructured":"Samet Akcay , Amir Atapour-Abarghouei , and Toby P Breckon . 2018 . Ganomaly: Semi-supervised anomaly detection via adversarial training . In Asian Conference on Computer Vision. Springer, 622--637 . Samet Akcay, Amir Atapour-Abarghouei, and Toby P Breckon. 2018. Ganomaly: Semi-supervised anomaly detection via adversarial training. In Asian Conference on Computer Vision. Springer, 622--637."},{"key":"e_1_3_2_1_3_1","volume-title":"Neural Machine Translation by Jointly Learning to Align and Translate. arXiv:1409.0473 [cs, stat] (May","author":"Bahdanau Dzmitry","year":"2016","unstructured":"Dzmitry Bahdanau , Kyunghyun Cho , and Yoshua Bengio . 2016. Neural Machine Translation by Jointly Learning to Align and Translate. arXiv:1409.0473 [cs, stat] (May 2016 ). http:\/\/arxiv.org\/abs\/1409.0473 arXiv: 1409.0473. Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2016. Neural Machine Translation by Jointly Learning to Align and Translate. arXiv:1409.0473 [cs, stat] (May 2016). http:\/\/arxiv.org\/abs\/1409.0473 arXiv: 1409.0473."},{"key":"e_1_3_2_1_4_1","volume-title":"An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling. arXiv:1803.01271","author":"Bai Shaojie","year":"2018","unstructured":"Shaojie Bai , J. Zico Kolter , and Vladlen Koltun . 2018. An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling. arXiv:1803.01271 ( 2018 ). Shaojie Bai, J. Zico Kolter, and Vladlen Koltun. 2018. An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling. arXiv:1803.01271 (2018)."},{"key":"e_1_3_2_1_5_1","first-page":"264","article-title":"The Holt-Winters Forecasting Procedure","volume":"27","author":"Chatfield C.","year":"1978","unstructured":"C. Chatfield . 1978 . The Holt-Winters Forecasting Procedure . Journal of the Royal Statistical Society 27 , 3 (1978), 264 -- 279 . C. Chatfield. 1978. The Holt-Winters Forecasting Procedure. Journal of the Royal Statistical Society 27, 3 (1978), 264--279.","journal-title":"Journal of the Royal Statistical Society"},{"key":"e_1_3_2_1_6_1","volume-title":"DAEMON: Unsupervised Anomaly Detection and Interpretation for Multivariate Time Series. In 2021 IEEE 37th International Conference on Data Engineering (ICDE). IEEE.","author":"Chen Xuanhao","year":"2021","unstructured":"Xuanhao Chen , Liwei Deng , Feiteng Huang , Chengwei Zhang , Zongquan Zhang , Yan Zhao , and Kai Zheng . 2021 . DAEMON: Unsupervised Anomaly Detection and Interpretation for Multivariate Time Series. In 2021 IEEE 37th International Conference on Data Engineering (ICDE). IEEE. Xuanhao Chen, Liwei Deng, Feiteng Huang, Chengwei Zhang, Zongquan Zhang, Yan Zhao, and Kai Zheng. 2021. DAEMON: Unsupervised Anomaly Detection and Interpretation for Multivariate Time Series. In 2021 IEEE 37th International Conference on Data Engineering (ICDE). IEEE."},{"key":"e_1_3_2_1_7_1","first-page":"3","article-title":"STL: A seasonal-trend decomposition","volume":"6","author":"Cleveland Robert B.","year":"1990","unstructured":"Robert B. Cleveland , William S. Cleveland , Jean E. McRae , and Irma Terpenning . 1990 . STL: A seasonal-trend decomposition . Journal of Official Statistics 6 , 1 (1990), 3 -- 73 . Robert B. Cleveland, William S. Cleveland, Jean E. McRae, and Irma Terpenning. 1990. STL: A seasonal-trend decomposition. Journal of Official Statistics 6, 1 (1990), 3--73.","journal-title":"Journal of Official Statistics"},{"key":"e_1_3_2_1_8_1","volume-title":"Historical Inertia: An Ignored but Powerful Baseline for Long Sequence Time-series Forecasting. arXiv:2103.16349 (March","author":"Cui Yue","year":"2021","unstructured":"Yue Cui , Jiandong Xie , and Kai Zheng . 2021 . Historical Inertia: An Ignored but Powerful Baseline for Long Sequence Time-series Forecasting. arXiv:2103.16349 (March 2021). http:\/\/arxiv.org\/abs\/2103.16349 arXiv: 2103.16349. Yue Cui, Jiandong Xie, and Kai Zheng. 2021. Historical Inertia: An Ignored but Powerful Baseline for Long Sequence Time-series Forecasting. arXiv:2103.16349 (March 2021). http:\/\/arxiv.org\/abs\/2103.16349 arXiv: 2103.16349."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1002\/for.3980040103"},{"key":"e_1_3_2_1_10_1","first-page":"3","article-title":"A Multimodal Anomaly Detector for Robot-Assisted Feeding Using An Lstm-Based Variational Autoencoder","volume":"3","author":"Guo Shengnan","year":"2018","unstructured":"Shengnan Guo , Youfang Lin , Ning Feng , Chao Song , and Huaiyu Wan . 2018 . A Multimodal Anomaly Detector for Robot-Assisted Feeding Using An Lstm-Based Variational Autoencoder . IEEE Robotics and Automation Letters 3 , 3 (July 2018), 1544--1551. Shengnan Guo, Youfang Lin, Ning Feng, Chao Song, and Huaiyu Wan. 2018. A Multimodal Anomaly Detector for Robot-Assisted Feeding Using An Lstm-Based Variational Autoencoder. IEEE Robotics and Automation Letters 3, 3 (July 2018), 1544--1551.","journal-title":"IEEE Robotics and Automation Letters"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301922"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301922"},{"key":"e_1_3_2_1_13_1","first-page":"27","article-title":"REGRESSION","volume":"7","author":"Hannan E. J.","year":"2010","unstructured":"E. J. Hannan and L. Kavalieris . 2010 . REGRESSION , AUTOREGRESSION MODELS. Journal of Time 7 , 1 (2010), 27 -- 49 . E. J. Hannan and L. Kavalieris. 2010. REGRESSION, AUTOREGRESSION MODELS. Journal of Time 7, 1 (2010), 27--49.","journal-title":"Journal of Time"},{"key":"e_1_3_2_1_14_1","volume-title":"Deep Residual Learning for Image Recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE","author":"He Kaiming","year":"2016","unstructured":"Kaiming He , Xiangyu Zhang , Shaoqing Ren , and Jian Sun . 2016 . Deep Residual Learning for Image Recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE , Las Vegas, NV, USA, 770--778. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep Residual Learning for Image Recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, Las Vegas, NV, USA, 770--778."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219845"},{"key":"e_1_3_2_1_16_1","volume-title":"Proceedings 2001 IEEE International Conference on Data Mining.","author":"Kalpakis K.","unstructured":"K. Kalpakis , D. Gada , and V. Puttagunta . 2002. Distance measures for effective clustering of ARIMA time-series . In Proceedings 2001 IEEE International Conference on Data Mining. K. Kalpakis, D. Gada, and V. Puttagunta. 2002. Distance measures for effective clustering of ARIMA time-series. In Proceedings 2001 IEEE International Conference on Data Mining."},{"key":"e_1_3_2_1_17_1","volume-title":"Kingma and Max Welling","author":"Diederik","year":"2014","unstructured":"Diederik P. Kingma and Max Welling . 2014 . Auto-Encoding Variational Bayes . arXiv:1312.6114 [cs, stat] (May 2014). http:\/\/arxiv.org\/abs\/1312.6114 arXiv: 1312.6114. Diederik P. Kingma and Max Welling. 2014. Auto-Encoding Variational Bayes. arXiv:1312.6114 [cs, stat] (May 2014). http:\/\/arxiv.org\/abs\/1312.6114 arXiv: 1312.6114."},{"key":"e_1_3_2_1_18_1","volume-title":"Kipf and Max Welling","author":"Thomas","year":"2017","unstructured":"Thomas N. Kipf and Max Welling . 2017 . Semi-Supervised Classification with Graph Convolutional Networks . arXiv:1609.02907 [cs, stat] (Feb. 2017). http:\/\/arxiv.org\/abs\/1609.02907 arXiv: 1609.02907. Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. arXiv:1609.02907 [cs, stat] (Feb. 2017). http:\/\/arxiv.org\/abs\/1609.02907 arXiv: 1609.02907."},{"key":"e_1_3_2_1_19_1","volume-title":"Reformer: The Efficient Transformer. arXiv:2001.04451 [cs, stat] (Feb.","author":"Kitaev Nikita","year":"2020","unstructured":"Nikita Kitaev , \u00e7ukasz Kaiser , and Anselm Levskaya . 2020 . Reformer: The Efficient Transformer. arXiv:2001.04451 [cs, stat] (Feb. 2020). http:\/\/arxiv.org\/abs\/2001. 04451 arXiv: 2001.04451. Nikita Kitaev, \u00e7ukasz Kaiser, and Anselm Levskaya. 2020. Reformer: The Efficient Transformer. arXiv:2001.04451 [cs, stat] (Feb. 2020). http:\/\/arxiv.org\/abs\/2001. 04451 arXiv: 2001.04451."},{"key":"e_1_3_2_1_20_1","volume-title":"The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval","author":"Lai Guokun","unstructured":"Guokun Lai , Wei-Cheng Chang , Yiming Yang , and Hanxiao Liu . 2018. Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks . In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval . ACM , Ann Arbor MI USA , 95--104. Guokun Lai, Wei-Cheng Chang, Yiming Yang, and Hanxiao Liu. 2018. Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. ACM, Ann Arbor MI USA, 95--104."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2788611"},{"key":"e_1_3_2_1_22_1","volume-title":"MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks. arXiv:1901.04997 [cs, stat] (Jan","author":"Li Dan","year":"2019","unstructured":"Dan Li , Dacheng Chen , Lei Shi , Baihong Jin , Jonathan Goh , and See-Kiong Ng. 2019. MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks. arXiv:1901.04997 [cs, stat] (Jan . 2019 ). Dan Li, Dacheng Chen, Lei Shi, Baihong Jin, Jonathan Goh, and See-Kiong Ng. 2019. MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks. arXiv:1901.04997 [cs, stat] (Jan. 2019)."},{"key":"e_1_3_2_1_23_1","volume-title":"Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting. arXiv:1907.00235 [cs, stat] (Jan","author":"Li Shiyang","year":"2020","unstructured":"Shiyang Li , Xiaoyong Jin , Yao Xuan , Xiyou Zhou , Wenhu Chen , Yu-Xiang Wang , and Xifeng Yan . 2020. Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting. arXiv:1907.00235 [cs, stat] (Jan . 2020 ). http:\/\/arxiv.org\/abs\/1907.00235 Shiyang Li, Xiaoyong Jin, Yao Xuan, Xiyou Zhou, Wenhu Chen, Yu-Xiang Wang, and Xifeng Yan. 2020. Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting. arXiv:1907.00235 [cs, stat] (Jan. 2020). http:\/\/arxiv.org\/abs\/1907.00235"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/2815675.2815679"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2019.04.014"},{"key":"e_1_3_2_1_26_1","volume-title":"LSTM-based Encoder-Decoder for Multisensor Anomaly Detection. arXiv:1607.00148 [cs, stat] (July","author":"Malhotra Pankaj","year":"2016","unstructured":"Pankaj Malhotra , Anusha Ramakrishnan , Gaurangi Anand , Lovekesh Vig , Puneet Agarwal , and Gautam Shroff . 2016. LSTM-based Encoder-Decoder for Multisensor Anomaly Detection. arXiv:1607.00148 [cs, stat] (July 2016 ). http:\/\/arxiv.org\/abs\/1607.00148 arXiv: 1607.00148. Pankaj Malhotra, Anusha Ramakrishnan, Gaurangi Anand, Lovekesh Vig, Puneet Agarwal, and Gautam Shroff. 2016. LSTM-based Encoder-Decoder for Multisensor Anomaly Detection. arXiv:1607.00148 [cs, stat] (July 2016). http:\/\/arxiv.org\/abs\/1607.00148 arXiv: 1607.00148."},{"key":"e_1_3_2_1_27_1","volume-title":"NBEATS: Neural basis expansion analysis for interpretable time series forecasting. arXiv:1905.10437 [cs, stat] (Feb.","author":"Oreshkin Boris N.","year":"2020","unstructured":"Boris N. Oreshkin , Dmitri Carpov , Nicolas Chapados , and Yoshua Bengio . 2020 . NBEATS: Neural basis expansion analysis for interpretable time series forecasting. arXiv:1905.10437 [cs, stat] (Feb. 2020). http:\/\/arxiv.org\/abs\/1905.10437 arXiv: 1905.10437. Boris N. Oreshkin, Dmitri Carpov, Nicolas Chapados, and Yoshua Bengio. 2020. NBEATS: Neural basis expansion analysis for interpretable time series forecasting. arXiv:1905.10437 [cs, stat] (Feb. 2020). http:\/\/arxiv.org\/abs\/1905.10437 arXiv: 1905.10437."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2008.2005605"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5438"},{"key":"e_1_3_2_1_30_1","volume-title":"Unsupervised Learning of Video Representations using LSTMs. arXiv:1502.04681 [cs] (Jan","author":"Srivastava Nitish","year":"2016","unstructured":"Nitish Srivastava , Elman Mansimov , and Ruslan Salakhutdinov . 2016. Unsupervised Learning of Video Representations using LSTMs. arXiv:1502.04681 [cs] (Jan . 2016 ). http:\/\/arxiv.org\/abs\/1502.04681 arXiv: 1502.04681. Nitish Srivastava, Elman Mansimov, and Ruslan Salakhutdinov. 2016. Unsupervised Learning of Video Representations using LSTMs. arXiv:1502.04681 [cs] (Jan. 2016). http:\/\/arxiv.org\/abs\/1502.04681 arXiv: 1502.04681."},{"key":"e_1_3_2_1_31_1","volume-title":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, Anchorage Alaska USA, 2828--2837","author":"Niu Chenhao","year":"2019","unstructured":"ya su, youjian zhao, Chenhao Niu , Rong Liu , Wei Sun , and Dan Pei . 2019 . Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network . In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, Anchorage Alaska USA, 2828--2837 . ya su, youjian zhao, Chenhao Niu, Rong Liu, Wei Sun, and Dan Pei. 2019. Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, Anchorage Alaska USA, 2828--2837."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1080\/00031305.2017.1380080"},{"key":"e_1_3_2_1_33_1","volume-title":"arXiv:1706.03762 [cs] (Dec","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani , Noam Shazeer , Niki Parmar , Jakob Uszkoreit , Llion Jones , Aidan N. Gomez , Lukasz Kaiser , and Illia Polosukhin . 2017. Attention Is All You Need. arXiv:1706.03762 [cs] (Dec . 2017 ). http:\/\/arxiv.org\/abs\/1706.03762 arXiv: 1706.03762. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention Is All You Need. arXiv:1706.03762 [cs] (Dec. 2017). http:\/\/arxiv.org\/abs\/1706.03762 arXiv: 1706.03762."},{"key":"e_1_3_2_1_34_1","volume-title":"Graph Attention Networks. arXiv:1710.10903 [cs, stat] (Feb","author":"Velickovi\u0107 Petar","year":"2018","unstructured":"Petar Velickovi\u0107 , Guillem Cucurull , Arantxa Casanova , Adriana Romero , Pietro Li\u00f2 , and Yoshua Bengio . 2018. Graph Attention Networks. arXiv:1710.10903 [cs, stat] (Feb . 2018 ). http:\/\/arxiv.org\/abs\/1710.10903 arXiv: 1710.10903. Petar Velickovi\u0107, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Li\u00f2, and Yoshua Bengio. 2018. Graph Attention Networks. arXiv:1710.10903 [cs, stat] (Feb. 2018). http:\/\/arxiv.org\/abs\/1710.10903 arXiv: 1710.10903."},{"key":"e_1_3_2_1_35_1","volume-title":"Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting. arXiv:2106.13008 [cs.LG] (Jan.","author":"Wu Haixu","year":"2022","unstructured":"Haixu Wu , Jiehui Xu , Jianmin Wang , and Mingsheng Long . 2022 . Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting. arXiv:2106.13008 [cs.LG] (Jan. 2022). https:\/\/arxiv.org\/abs\/2106.13008 arXiv:2106.13008. Haixu Wu, Jiehui Xu, Jianmin Wang, and Mingsheng Long. 2022. Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting. arXiv:2106.13008 [cs.LG] (Jan. 2022). https:\/\/arxiv.org\/abs\/2106.13008 arXiv:2106.13008."},{"key":"e_1_3_2_1_36_1","volume-title":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, Virtual Event CA USA, 753--763","author":"Pan Shirui","year":"2020","unstructured":"ZonghanWu, Shirui Pan , Guodong Long , Jing Jiang , Xiaojun Chang , and Chengqi Zhang . 2020 . Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks . In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, Virtual Event CA USA, 753--763 . ZonghanWu, Shirui Pan, Guodong Long, Jing Jiang, Xiaojun Chang, and Chengqi Zhang. 2020. Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, Virtual Event CA USA, 753--763."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3185996"},{"key":"e_1_3_2_1_38_1","volume-title":"Spatial-Temporal Convolutional Graph Attention Networks for Citywide Traffic Flow Forecasting. In CIKM '20: The 29th ACM International Conference on Information and Knowledge Management.","author":"Zhang X.","unstructured":"X. Zhang , C. Huang , Y. Xu , and L. Xia . 2020 . Spatial-Temporal Convolutional Graph Attention Networks for Citywide Traffic Flow Forecasting. In CIKM '20: The 29th ACM International Conference on Information and Knowledge Management. X. Zhang, C. Huang, Y. Xu, and L. Xia. 2020. Spatial-Temporal Convolutional Graph Attention Networks for Citywide Traffic Flow Forecasting. In CIKM '20: The 29th ACM International Conference on Information and Knowledge Management."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/616"},{"key":"e_1_3_2_1_40_1","volume-title":"Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting. In The Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI","author":"Zhou Haoyi","year":"2021","unstructured":"Haoyi Zhou , Shanghang Zhang , Jieqi Peng , Shuai Zhang , Jianxin Li , Hui Xiong , and Wancai Zhang . 2021 . Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting. In The Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021. AAAI Press, online. Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting. In The Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021. AAAI Press, online."},{"key":"e_1_3_2_1_41_1","volume-title":"Wei Cheng, Cristian Lumezanu, Daeki Cho, and Haifeng Chen.","author":"Zong Bo","year":"2018","unstructured":"Bo Zong , Qi Song , Martin Renqiang Min , Wei Cheng, Cristian Lumezanu, Daeki Cho, and Haifeng Chen. 2018 . Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection . (2018), 1--19. Bo Zong, Qi Song, Martin Renqiang Min, Wei Cheng, Cristian Lumezanu, Daeki Cho, and Haifeng Chen. 2018. Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection. (2018), 1--19."}],"event":{"name":"CIKM '22: The 31st ACM International Conference on Information and Knowledge Management","location":"Atlanta GA USA","acronym":"CIKM '22","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 31st ACM International Conference on Information & Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3511808.3557386","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3511808.3557386","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:48:54Z","timestamp":1750182534000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3511808.3557386"}},"subtitle":["An MLP-Attention Model for Multivariate Time-Series Analysis"],"short-title":[],"issued":{"date-parts":[[2022,10,17]]},"references-count":41,"alternative-id":["10.1145\/3511808.3557386","10.1145\/3511808"],"URL":"https:\/\/doi.org\/10.1145\/3511808.3557386","relation":{},"subject":[],"published":{"date-parts":[[2022,10,17]]},"assertion":[{"value":"2022-10-17","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}