{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T16:39:30Z","timestamp":1781714370579,"version":"3.54.5"},"publisher-location":"New York, NY, USA","reference-count":32,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T00:00:00Z","timestamp":1729468800000},"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":[[2024,10,21]]},"DOI":"10.1145\/3627673.3679672","type":"proceedings-article","created":{"date-parts":[[2024,10,20]],"date-time":"2024-10-20T19:34:21Z","timestamp":1729452861000},"page":"1235-1244","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["LagCNN: A Fast yet Effective Model for Multivariate Long-term Time Series Forecasting"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0967-8160","authenticated-orcid":false,"given":"Linsen","family":"Li","sequence":"first","affiliation":[{"name":"Zhejiang University & Hikvision Research Institute, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-4922-9708","authenticated-orcid":false,"given":"Chunfei","family":"Jian","sequence":"additional","affiliation":[{"name":"Hikvision Research Institute, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1791-3020","authenticated-orcid":false,"given":"Feng","family":"Wan","sequence":"additional","affiliation":[{"name":"Hikvision Research Institute, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5325-3296","authenticated-orcid":false,"given":"Dongdong","family":"Geng","sequence":"additional","affiliation":[{"name":"Hikvision Research Institute, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-2034-5501","authenticated-orcid":false,"given":"Ziquan","family":"Fang","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5685-7017","authenticated-orcid":false,"given":"Lu","family":"Chen","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3816-8450","authenticated-orcid":false,"given":"Yunjun","family":"Gao","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3482-8538","authenticated-orcid":false,"given":"Weihao","family":"Jiang","sequence":"additional","affiliation":[{"name":"Hikvision Research Institute, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-0877-0490","authenticated-orcid":false,"given":"Jiang","family":"Zhu","sequence":"additional","affiliation":[{"name":"Hikvision Research Institute, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,10,21]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Neamat El Gayar, and Hisham El-Shishiny","author":"Ahmed Nesreen K","year":"2010","unstructured":"Nesreen K Ahmed, Amir F Atiya, Neamat El Gayar, and Hisham El-Shishiny. 2010. An empirical comparison of machine learning models for time series forecasting. Econometric reviews, Vol. 29, 5--6 (2010), 594--621."},{"key":"e_1_3_2_1_2_1","volume-title":"An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint 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 preprint arXiv:1803.01271 (2018)."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i6.25854"},{"key":"e_1_3_2_1_4_1","volume-title":"Long-term Forecasting with","author":"Das Abhimanyu","year":"2023","unstructured":"Abhimanyu Das, Weihao Kong, Andrew Leach, Rajat Sen, and Rose Yu. 2023. Long-term Forecasting with TiDE: Time-series Dense Encoder. arXiv preprint arXiv:2304.08424 (2023)."},{"key":"e_1_3_2_1_5_1","volume-title":"Greedy function approximation: a gradient boosting machine. Annals of statistics","author":"Friedman Jerome H","year":"2001","unstructured":"Jerome H Friedman. 2001. Greedy function approximation: a gradient boosting machine. Annals of statistics (2001), 1189--1232."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/for.3980040103"},{"key":"e_1_3_2_1_7_1","first-page":"5816","article-title":"Scinet: Time series modeling and forecasting with sample convolution and interaction","volume":"35","author":"Liu Minhao","year":"2022","unstructured":"Minhao Liu, Ailing Zeng, Muxi Chen, Zhijian Xu, Qiuxia Lai, Lingna Ma, and Qiang Xu. 2022. Scinet: Time series modeling and forecasting with sample convolution and interaction. Advances in Neural Information Processing Systems, Vol. 35 (2022), 5816--5828.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_8_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_1_9_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_1_10_1","first-page":"9881","article-title":"Non-stationary transformers: Exploring the stationarity in time series forecasting","volume":"35","author":"Liu Yong","year":"2022","unstructured":"Yong Liu, Haixu Wu, Jianmin Wang, and Mingsheng Long. 2022. Non-stationary transformers: Exploring the stationarity in time series forecasting. Advances in Neural Information Processing Systems, Vol. 35 (2022), 9881--9893.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/EPEC47565.2019.9074776"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1553-2712.1998.tb02493.x"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1002\/for.3980020104"},{"key":"e_1_3_2_1_14_1","volume-title":"The Eleventh International Conference on Learning Representations.","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. In The Eleventh International Conference on Learning Representations."},{"key":"e_1_3_2_1_15_1","volume-title":"International Conference on Learning Representations.","author":"Oreshkin Boris N","year":"2019","unstructured":"Boris N Oreshkin, Dmitri Carpov, Nicolas Chapados, and Yoshua Bengio. 2019. N-BEATS: Neural basis expansion analysis for interpretable time series forecasting. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_16_1","volume-title":"Deep state space models for time series forecasting. Advances in neural information processing systems","author":"Rangapuram Syama Sundar","year":"2018","unstructured":"Syama Sundar Rangapuram, Matthias W Seeger, Jan Gasthaus, Lorenzo Stella, Yuyang Wang, and Tim Januschowski. 2018. Deep state space models for time series forecasting. Advances in neural information processing systems, Vol. 31 (2018)."},{"key":"e_1_3_2_1_17_1","volume-title":"Arian Khorasani, George Adamopoulos, Rishika Bhagwatkar, Marin Bilovs, Hena Ghonia, Nadhir Vincent Hassen, Anderson Schneider, et al.","author":"Rasul Kashif","year":"2023","unstructured":"Kashif Rasul, Arjun Ashok, Andrew Robert Williams, Arian Khorasani, George Adamopoulos, Rishika Bhagwatkar, Marin Bilovs, Hena Ghonia, Nadhir Vincent Hassen, Anderson Schneider, et al. 2023. Lag-llama: Towards foundation models for time series forecasting. arXiv preprint arXiv:2310.08278 (2023)."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2019.07.001"},{"key":"e_1_3_2_1_19_1","volume-title":"Application of support vector machines in financial time series forecasting. omega","author":"Tay Francis EH","year":"2001","unstructured":"Francis EH Tay and Lijuan Cao. 2001. Application of support vector machines in financial time series forecasting. omega, Vol. 29, 4 (2001), 309--317."},{"key":"e_1_3_2_1_20_1","volume-title":"TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.","author":"Vijay E","year":"2023","unstructured":"E Vijay, Arindam Jati, Nam Nguyen, Gift Sinthong, and Jayant Kalagnanam. 2023. TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining."},{"key":"e_1_3_2_1_21_1","volume-title":"The Eleventh International Conference on Learning Representations.","author":"Wang Huiqiang","year":"2022","unstructured":"Huiqiang Wang, Jian Peng, Feihu Huang, Jince Wang, Junhui Chen, and Yifei Xiao. 2022. Micn: Multi-scale local and global context modeling for long-term series forecasting. In The Eleventh International Conference on Learning Representations."},{"key":"e_1_3_2_1_22_1","volume-title":"MPPN: Multi-Resolution Periodic Pattern Network For Long-Term Time Series Forecasting. arXiv preprint arXiv:2306.06895","author":"Wang Xing","year":"2023","unstructured":"Xing Wang, Zhendong Wang, Kexin Yang, Junlan Feng, Zhiyan Song, Chao Deng, et al. 2023. MPPN: Multi-Resolution Periodic Pattern Network For Long-Term Time Series Forecasting. arXiv preprint arXiv:2306.06895 (2023)."},{"key":"e_1_3_2_1_23_1","volume-title":"A multi-horizon quantile recurrent forecaster. arXiv preprint arXiv:1711.11053","author":"Wen Ruofeng","year":"2017","unstructured":"Ruofeng Wen, Kari Torkkola, Balakrishnan Narayanaswamy, and Dhruv Madeka. 2017. A multi-horizon quantile recurrent forecaster. arXiv preprint arXiv:1711.11053 (2017)."},{"key":"e_1_3_2_1_24_1","volume-title":"Etsformer: Exponential smoothing transformers for time-series forecasting. arXiv preprint arXiv:2202.01381","author":"Woo Gerald","year":"2022","unstructured":"Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, and Steven Hoi. 2022. Etsformer: Exponential smoothing transformers for time-series forecasting. arXiv preprint arXiv:2202.01381 (2022)."},{"key":"e_1_3_2_1_25_1","volume-title":"The Eleventh International Conference on Learning Representations.","author":"Wu Haixu","year":"2022","unstructured":"Haixu Wu, Tengge Hu, Yong Liu, Hang Zhou, Jianmin Wang, and Mingsheng Long. 2022. TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis. In The Eleventh International Conference on Learning Representations."},{"key":"e_1_3_2_1_26_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_1_27_1","volume-title":"Multi-step ahead time series forecasting for different data patterns based on LSTM recurrent neural network. In 2017 14th web information systems and applications conference (WISA)","author":"Yunpeng Liu","unstructured":"Liu Yunpeng, Hou Di, Bao Junpeng, and Qi Yong. 2017. Multi-step ahead time series forecasting for different data patterns based on LSTM recurrent neural network. In 2017 14th web information systems and applications conference (WISA). IEEE, 305--310."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i9.26317"},{"key":"e_1_3_2_1_29_1","volume-title":"Less is more: Fast multivariate time series forecasting with light sampling-oriented mlp structures. arXiv preprint arXiv:2207.01186","author":"Zhang Tianping","year":"2022","unstructured":"Tianping Zhang, Yizhuo Zhang, Wei Cao, Jiang Bian, Xiaohan Yi, Shun Zheng, and Jian Li. 2022. Less is more: Fast multivariate time series forecasting with light sampling-oriented mlp structures. arXiv preprint arXiv:2207.01186 (2022)."},{"key":"e_1_3_2_1_30_1","volume-title":"The Eleventh International Conference on Learning Representations.","author":"Zhang Yunhao","year":"2022","unstructured":"Yunhao Zhang and Junchi Yan. 2022. Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting. In The Eleventh International Conference on Learning Representations."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"e_1_3_2_1_32_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."}],"event":{"name":"CIKM '24: The 33rd ACM International Conference on Information and Knowledge Management","location":"Boise ID USA","acronym":"CIKM '24","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 33rd ACM International Conference on Information and Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3627673.3679672","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3627673.3679672","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:58:13Z","timestamp":1750294693000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3627673.3679672"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,21]]},"references-count":32,"alternative-id":["10.1145\/3627673.3679672","10.1145\/3627673"],"URL":"https:\/\/doi.org\/10.1145\/3627673.3679672","relation":{},"subject":[],"published":{"date-parts":[[2024,10,21]]},"assertion":[{"value":"2024-10-21","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}