{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T18:00:04Z","timestamp":1775325604012,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":68,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,8,24]],"date-time":"2024-08-24T00:00:00Z","timestamp":1724457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,8,25]]},"DOI":"10.1145\/3637528.3671881","type":"proceedings-article","created":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T04:55:12Z","timestamp":1724561712000},"page":"3919-3930","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["RPMixer: Shaking Up Time Series Forecasting with Random Projections for Large Spatial-Temporal Data"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9807-2963","authenticated-orcid":false,"given":"Chin-Chia Michael","family":"Yeh","sequence":"first","affiliation":[{"name":"Visa Research, Foster City, CA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2635-9420","authenticated-orcid":false,"given":"Yujie","family":"Fan","sequence":"additional","affiliation":[{"name":"Visa Research, Foster City, CA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-6218-1737","authenticated-orcid":false,"given":"Xin","family":"Dai","sequence":"additional","affiliation":[{"name":"Visa Research, Foster City, CA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8561-5527","authenticated-orcid":false,"given":"Uday Singh","family":"Saini","sequence":"additional","affiliation":[{"name":"Visa Research, Foster City, CA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3165-6136","authenticated-orcid":false,"given":"Vivian","family":"Lai","sequence":"additional","affiliation":[{"name":"Visa Research, Foster City, CA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3030-5477","authenticated-orcid":false,"given":"Prince Osei","family":"Aboagye","sequence":"additional","affiliation":[{"name":"Visa Research, Foster City, CA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1130-9914","authenticated-orcid":false,"given":"Junpeng","family":"Wang","sequence":"additional","affiliation":[{"name":"Visa Research, Foster City, CA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6360-558X","authenticated-orcid":false,"given":"Huiyuan","family":"Chen","sequence":"additional","affiliation":[{"name":"Visa Research, Foster City, CA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4422-9889","authenticated-orcid":false,"given":"Yan","family":"Zheng","sequence":"additional","affiliation":[{"name":"Visa Research, Foster City, CA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6717-5102","authenticated-orcid":false,"given":"Zhongfang","family":"Zhuang","sequence":"additional","affiliation":[{"name":"Visa Research, Foster City, CA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3881-1833","authenticated-orcid":false,"given":"Liang","family":"Wang","sequence":"additional","affiliation":[{"name":"Visa Research, Foster City, CA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-7984-7241","authenticated-orcid":false,"given":"Wei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Visa Research, Foster City, CA, USA"}]}],"member":"320","published-online":{"date-parts":[[2024,8,24]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Aaron Bostrom, James Large, and Eamonn Keogh.","author":"Bagnall Anthony","year":"2017","unstructured":"Anthony Bagnall, Jason Lines, Aaron Bostrom, James Large, and Eamonn Keogh. 2017. The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data mining and knowledge discovery, Vol. 31 (2017), 606--660."},{"key":"e_1_3_2_2_2_1","volume-title":"Adaptive graph convolutional recurrent network for traffic forecasting. Advances in neural information processing systems","author":"Bai Lei","year":"2020","unstructured":"Lei Bai, Lina Yao, Can Li, Xianzhi Wang, and Can Wang. 2020. Adaptive graph convolutional recurrent network for traffic forecasting. Advances in neural information processing systems, Vol. 33 (2020), 17804--17815."},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/502512.502546"},{"key":"e_1_3_2_2_4_1","volume-title":"MemDA: Forecasting Urban Time Series with Memory-based Drift Adaptation. arXiv preprint arXiv:2309.14216","author":"Cai Zekun","year":"2023","unstructured":"Zekun Cai, Renhe Jiang, Xinyu Yang, Zhaonan Wang, Diansheng Guo, Hiroki Kobayashi, Xuan Song, and Ryosuke Shibasaki. 2023. MemDA: Forecasting Urban Time Series with Memory-based Drift Adaptation. arXiv preprint arXiv:2309.14216 (2023)."},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1111\/rssb.12228"},{"key":"e_1_3_2_2_6_1","volume-title":"Tsmixer: An all-mlp architecture for time series forecasting. arXiv preprint arXiv:2303.06053","author":"Chen Si-An","year":"2023","unstructured":"Si-An Chen, Chun-Liang Li, Nate Yoder, Sercan O Arik, and Tomas Pfister. 2023. Tsmixer: An all-mlp architecture for time series forecasting. arXiv preprint arXiv:2303.06053 (2023)."},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/JAS.2019.1911747"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-020-00701-z"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3615066"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467430"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33013656"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301922"},{"key":"e_1_3_2_2_13_1","volume-title":"Proceedings, Part IV 14","author":"He Kaiming","year":"2016","unstructured":"Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Identity mappings in deep residual networks. In Computer Vision--ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11--14, 2016, Proceedings, Part IV 14. Springer, 630--645."},{"key":"e_1_3_2_2_14_1","volume-title":"Long short-term memory. Neural computation","author":"Hochreiter Sepp","year":"1997","unstructured":"Sepp Hochreiter and J\u00fcrgen Schmidhuber. 1997. Long short-term memory. Neural computation, Vol. 9, 8 (1997), 1735--1780."},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2014.10.001"},{"key":"e_1_3_2_2_16_1","volume-title":"Deep learning for time series classification: a review. Data mining and knowledge discovery","author":"Fawaz Hassan Ismail","year":"2019","unstructured":"Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber, Lhassane Idoumghar, and Pierre-Alain Muller. 2019. Deep learning for time series classification: a review. Data mining and knowledge discovery, Vol. 33, 4 (2019), 917--963."},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1090\/conm\/026\/737400"},{"key":"e_1_3_2_2_18_1","volume-title":"Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907","author":"Kipf Thomas N","year":"2016","unstructured":"Thomas N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)."},{"key":"e_1_3_2_2_19_1","volume-title":"International conference on machine learning. PMLR, 11906--11917","author":"Lan Shiyong","year":"2022","unstructured":"Shiyong Lan, Yitong Ma, Weikang Huang, Wenwu Wang, Hongyu Yang, and Pyang Li. 2022. Dstagnn: Dynamic spatial-temporal aware graph neural network for traffic flow forecasting. In International conference on machine learning. PMLR, 11906--11917."},{"key":"e_1_3_2_2_20_1","volume-title":"Dynamic graph convolutional recurrent network for traffic prediction: Benchmark and solution. ACM Transactions on Knowledge Discovery from Data","author":"Li Fuxian","year":"2023","unstructured":"Fuxian Li, Jie Feng, Huan Yan, Guangyin Jin, Fan Yang, Funing Sun, Depeng Jin, and Yong Li. 2023. Dynamic graph convolutional recurrent network for traffic prediction: Benchmark and solution. ACM Transactions on Knowledge Discovery from Data, Vol. 17, 1 (2023), 1--21."},{"key":"e_1_3_2_2_21_1","volume-title":"Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting. Advances in neural information processing systems","author":"Li Shiyang","year":"2019","unstructured":"Shiyang Li, Xiaoyong Jin, Yao Xuan, Xiyou Zhou, Wenhu Chen, Yu-Xiang Wang, and Xifeng Yan. 2019. Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting. Advances in neural information processing systems, Vol. 32 (2019)."},{"key":"e_1_3_2_2_22_1","volume-title":"Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv preprint arXiv:1707.01926","author":"Li Yaguang","year":"2017","unstructured":"Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu. 2017. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv preprint arXiv:1707.01926 (2017)."},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2021.03.012"},{"key":"e_1_3_2_2_24_1","volume-title":"International conference on learning representations.","author":"Liu Shizhan","year":"2021","unstructured":"Shizhan Liu, Hang Yu, Cong Liao, Jianguo Li, Weiyao Lin, Alex X Liu, and Schahram Dustdar. 2021. Pyraformer: Low-complexity pyramidal attention for long-range time series modeling and forecasting. In International conference on learning representations."},{"key":"e_1_3_2_2_25_1","volume-title":"LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting. arXiv preprint arXiv:2306.08259","author":"Liu Xu","year":"2023","unstructured":"Xu Liu, Yutong Xia, Yuxuan Liang, Junfeng Hu, Yiwei Wang, Lei Bai, Chao Huang, Zhenguang Liu, Bryan Hooi, and Roger Zimmermann. 2023. LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting. arXiv preprint arXiv:2306.08259 (2023)."},{"key":"e_1_3_2_2_26_1","volume-title":"itransformer: Inverted transformers are effective for time series forecasting. arXiv preprint arXiv:2310.06625","author":"Liu Yong","year":"2023","unstructured":"Yong Liu, Tengge Hu, Haoran Zhang, Haixu Wu, Shiyu Wang, Lintao Ma, and Mingsheng Long. 2023. itransformer: Inverted transformers are effective for time series forecasting. arXiv preprint arXiv:2310.06625 (2023)."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-60639-8_40"},{"key":"e_1_3_2_2_28_1","volume-title":"Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101","author":"Loshchilov Ilya","year":"2017","unstructured":"Ilya Loshchilov and Frank Hutter. 2017. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)."},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.5555\/645526.757762"},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-017-9593-z"},{"key":"e_1_3_2_2_31_1","volume-title":"A time series is worth 64 words: Long-term forecasting with transformers. arXiv preprint arXiv:2211.14730","author":"Nie Yuqi","year":"2022","unstructured":"Yuqi Nie, Nam H Nguyen, Phanwadee Sinthong, and Jayant Kalagnanam. 2022. A time series is worth 64 words: Long-term forecasting with transformers. arXiv preprint arXiv:2211.14730 (2022)."},{"key":"e_1_3_2_2_32_1","volume-title":"Wavenet: A generative model for raw audio. arXiv preprint arXiv:1609.03499","author":"van den Oord Aaron","year":"2016","unstructured":"Aaron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, and Koray Kavukcuoglu. 2016. Wavenet: A generative model for raw audio. arXiv preprint arXiv:1609.03499 (2016)."},{"key":"e_1_3_2_2_33_1","volume-title":"ECML\/PKDD Workshop on Advanced Analytics and Learning on Temporal Data.","author":"Renard Xavier","year":"2016","unstructured":"Xavier Renard, Maria Rifqi, Gabriel Fricout, and Marcin Detyniecki. 2016. EAST representation: fast discovery of discriminant temporal patterns from time series. In ECML\/PKDD Workshop on Advanced Analytics and Learning on Temporal Data."},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/CRV.2019.00010"},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-01347-8_26"},{"key":"e_1_3_2_2_36_1","volume-title":"Discrete graph structure learning for forecasting multiple time series. arXiv preprint arXiv:2101.06861","author":"Shang Chao","year":"2021","unstructured":"Chao Shang, Jie Chen, and Jinbo Bi. 2021. Discrete graph structure learning for forecasting multiple time series. arXiv preprint arXiv:2101.06861 (2021)."},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557702"},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"crossref","unstructured":"Zezhi Shao Zhao Zhang Fei Wang Wei Wei and Yongjun Xu. 2022. Spatial-temporal identity: A simple yet effective baseline for multivariate time series forecasting. https:\/\/github.com\/zezhishao\/STID.","DOI":"10.1145\/3511808.3557702"},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539396"},{"key":"e_1_3_2_2_40_1","volume-title":"2022 d. Decoupled dynamic spatial-temporal graph neural network for traffic forecasting. arXiv preprint arXiv:2206.09112","author":"Shao Zezhi","year":"2022","unstructured":"Zezhi Shao, Zhao Zhang, Wei Wei, Fei Wang, Yongjun Xu, Xin Cao, and Christian S Jensen. 2022 d. Decoupled dynamic spatial-temporal graph neural network for traffic forecasting. arXiv preprint arXiv:2206.09112 (2022)."},{"key":"e_1_3_2_2_41_1","unstructured":"The Author(s). 2023. Project Website. https:\/\/sites.google.com\/view\/rpmixer."},{"key":"e_1_3_2_2_42_1","volume-title":"Mlp-mixer: An all-mlp architecture for vision. Advances in neural information processing systems","author":"Tolstikhin Ilya O","year":"2021","unstructured":"Ilya O Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Andreas Steiner, Daniel Keysers, Jakob Uszkoreit, et al. 2021. Mlp-mixer: An all-mlp architecture for vision. Advances in neural information processing systems, Vol. 34 (2021), 24261--24272."},{"key":"e_1_3_2_2_43_1","volume-title":"Soroush Mehri, Negar Rostamzadeh, Yoshua Bengio, and Christopher J Pal.","author":"Trabelsi Chiheb","year":"2018","unstructured":"Chiheb Trabelsi, Olexa Bilaniuk, Ying Zhang, Dmitriy Serdyuk, Sandeep Subramanian, Jo\u00e3o Felipe Santos, Soroush Mehri, Negar Rostamzadeh, Yoshua Bengio, and Christopher J Pal. 2018. Deep Complex Networks. arxiv: 1705.09792 [cs.NE]"},{"key":"e_1_3_2_2_44_1","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition. 9446--9454","author":"Ulyanov Dmitry","year":"2018","unstructured":"Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky. 2018. Deep image prior. In Proceedings of the IEEE conference on computer vision and pattern recognition. 9446--9454."},{"key":"e_1_3_2_2_45_1","volume-title":"Attention is all you need. Advances in neural information processing systems","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems, Vol. 30 (2017)."},{"key":"e_1_3_2_2_46_1","volume-title":"Residual networks behave like ensembles of relatively shallow networks. Advances in neural information processing systems","author":"Veit Andreas","year":"2016","unstructured":"Andreas Veit, Michael J Wilber, and Serge Belongie. 2016. Residual networks behave like ensembles of relatively shallow networks. Advances in neural information processing systems, Vol. 29 (2016)."},{"key":"e_1_3_2_2_47_1","volume-title":"Graph attention networks. arXiv preprint arXiv:1710.10903","author":"Velivckovi\u0107 Petar","year":"2017","unstructured":"Petar Velivckovi\u0107, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)."},{"key":"e_1_3_2_2_48_1","volume-title":"Deepvid: Deep visual interpretation and diagnosis for image classifiers via knowledge distillation","author":"Wang Junpeng","year":"2019","unstructured":"Junpeng Wang, Liang Gou, Wei Zhang, Hao Yang, and Han-Wei Shen. 2019. Deepvid: Deep visual interpretation and diagnosis for image classifiers via knowledge distillation. IEEE transactions on visualization and computer graphics, Vol. 25, 6 (2019), 2168--2180."},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2021.3076749"},{"key":"e_1_3_2_2_50_1","first-page":"22419","article-title":"Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting","volume":"34","author":"Wu Haixu","year":"2021","unstructured":"Haixu Wu, Jiehui Xu, Jianmin Wang, and Mingsheng Long. 2021. Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting. Advances in Neural Information Processing Systems, Vol. 34 (2021), 22419--22430.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_51_1","volume-title":"Graph wavenet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121","author":"Wu Zonghan","year":"2019","unstructured":"Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhang. 2019. Graph wavenet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121 (2019)."},{"key":"e_1_3_2_2_52_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.12328"},{"key":"e_1_3_2_2_53_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11836"},{"key":"e_1_3_2_2_54_1","volume-title":"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4400--4404","author":"Michael Yeh Chin-Chia","year":"2023","unstructured":"Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Audrey Der, Vivian Lai, Zhongfang Zhuang, Junpeng Wang, Liang Wang, et al. 2023. Toward a foundation model for time series data. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 4400--4404."},{"key":"e_1_3_2_2_55_1","volume-title":"Multitask Learning for Time Series Data with 2D Convolution. arXiv preprint arXiv:2310.03925","author":"Michael Yeh Chin-Chia","year":"2023","unstructured":"Chin-Chia Michael Yeh, Xin Dai, Yan Zheng, Junpeng Wang, Huiyuan Chen, Yujie Fan, Audrey Der, Zhongfang Zhuang, Liang Wang, and Wei Zhang. 2023. Multitask Learning for Time Series Data with 2D Convolution. arXiv preprint arXiv:2310.03925 (2023)."},{"key":"e_1_3_2_2_56_1","volume-title":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391--4401","author":"Michael Yeh Chin-Chia","year":"2022","unstructured":"Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, and Wei Zhang. 2022. Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4391--4401."},{"key":"e_1_3_2_2_57_1","volume-title":"2017 IEEE international conference on data mining (ICDM). IEEE, 565--574","author":"Michael Yeh Chin-Chia","year":"2017","unstructured":"Chin-Chia Michael Yeh, Nickolas Kavantzas, and Eamonn Keogh. 2017. Matrix profile VI: Meaningful multidimensional motif discovery. In 2017 IEEE international conference on data mining (ICDM). IEEE, 565--574."},{"key":"e_1_3_2_2_58_1","unstructured":"Chin-Chia Michael Yeh and Eamonn Keogh. 2016. The First Place Solution to the AALTD'16 Challenge. https:\/\/github.com\/mcyeh\/aaltd16_fusion."},{"key":"e_1_3_2_2_59_1","volume-title":"Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393","author":"Michael Yeh Chin-Chia","year":"2023","unstructured":"Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M Phillips, and Eamonn Keogh. 2023. Sketching multidimensional time series for fast discord mining. arXiv preprint arXiv:2311.03393 (2023)."},{"key":"e_1_3_2_2_60_1","volume-title":"Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181--189","author":"Michael Yeh Chin-Chia","year":"2022","unstructured":"Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, and Eamonn Keogh. 2022. Error-bounded approximate time series joins using compact dictionary representations of time series. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 181--189."},{"key":"e_1_3_2_2_61_1","volume-title":"2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317--1322","author":"Michael Yeh Chin-Chia","year":"2016","unstructured":"Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317--1322."},{"key":"e_1_3_2_2_62_1","volume-title":"Towards a near universal time series data mining tool: Introducing the matrix profile","author":"Chin-Chia Yeh Michael","unstructured":"Michael Chin-Chia Yeh. 2018. Towards a near universal time series data mining tool: Introducing the matrix profile. University of California, Riverside."},{"key":"e_1_3_2_2_63_1","volume-title":"Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875","author":"Yu Bing","year":"2017","unstructured":"Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017)."},{"key":"e_1_3_2_2_64_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i9.26317"},{"key":"e_1_3_2_2_65_1","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3614969"},{"key":"e_1_3_2_2_66_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"e_1_3_2_2_67_1","volume-title":"International Conference on Machine Learning. PMLR, 27268--27286","author":"Zhou Tian","year":"2022","unstructured":"Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268--27286."},{"key":"e_1_3_2_2_68_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357223.3362721"}],"event":{"name":"KDD '24: The 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Barcelona Spain","acronym":"KDD '24","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3671881","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3637528.3671881","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:04:15Z","timestamp":1750291455000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3671881"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,24]]},"references-count":68,"alternative-id":["10.1145\/3637528.3671881","10.1145\/3637528"],"URL":"https:\/\/doi.org\/10.1145\/3637528.3671881","relation":{},"subject":[],"published":{"date-parts":[[2024,8,24]]},"assertion":[{"value":"2024-08-24","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}