{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T10:05:14Z","timestamp":1775815514106,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":36,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T00:00:00Z","timestamp":1691107200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFE0200500"],"award-info":[{"award-number":["2022YFE0200500"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"SJTU Global Strategic Partnership Fund","award":["2021 SJTU-HKUST"],"award-info":[{"award-number":["2021 SJTU-HKUST"]}]},{"name":"Shanghai Municipal Science and Technology Major Project","award":["2021SHZDZX0102"],"award-info":[{"award-number":["2021SHZDZX0102"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,8,6]]},"DOI":"10.1145\/3580305.3599315","type":"proceedings-article","created":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T18:13:58Z","timestamp":1691172838000},"page":"3492-3503","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":26,"title":["DoubleAdapt: A Meta-learning Approach to Incremental Learning for Stock Trend Forecasting"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3526-8579","authenticated-orcid":false,"given":"Lifan","family":"Zhao","sequence":"first","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8112-212X","authenticated-orcid":false,"given":"Shuming","family":"Kong","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8364-3674","authenticated-orcid":false,"given":"Yanyan","family":"Shen","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}]}],"member":"320","published-online":{"date-parts":[[2023,8,4]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"TaskNorm: Rethinking Batch Normalization for Meta-learning. In International Conference on Machine Learning. PMLR, 1153--1164","author":"Bronskill John","year":"2020","unstructured":"John Bronskill , Jonathan Gordon , James Requeima , Sebastian Nowozin , and Richard Turner . 2020 . TaskNorm: Rethinking Batch Normalization for Meta-learning. In International Conference on Machine Learning. PMLR, 1153--1164 . John Bronskill, Jonathan Gordon, James Requeima, Sebastian Nowozin, and Richard Turner. 2020. TaskNorm: Rethinking Batch Normalization for Meta-learning. In International Conference on Machine Learning. PMLR, 1153--1164."},{"key":"e_1_3_2_2_2_1","volume-title":"Irina Rish, Alexandre Lacoste, David V\u00e1zquez, and Laurent Charlin.","author":"Caccia Massimo","year":"2020","unstructured":"Massimo Caccia , Pau Rodr\u00edguez , Oleksiy Ostapenko , Fabrice Normandin , Min Lin , Lucas Page-Caccia , Issam Hadj Laradji , Irina Rish, Alexandre Lacoste, David V\u00e1zquez, and Laurent Charlin. 2020 . Online Fast Adaptation and Knowledge Accumulation (OSAKA): a New Approach to Continual Learning. In NeurIPS. https:\/\/proceedings.neurips.cc\/paper\/2020\/hash\/ c0a271bc0ecb776a094786474322cb82-Abstract.html Massimo Caccia, Pau Rodr\u00edguez, Oleksiy Ostapenko, Fabrice Normandin, Min Lin, Lucas Page-Caccia, Issam Hadj Laradji, Irina Rish, Alexandre Lacoste, David V\u00e1zquez, and Laurent Charlin. 2020. Online Fast Adaptation and Knowledge Accumulation (OSAKA): a New Approach to Continual Learning. In NeurIPS. https:\/\/proceedings.neurips.cc\/paper\/2020\/hash\/ c0a271bc0ecb776a094786474322cb82-Abstract.html"},{"key":"e_1_3_2_2_3_1","volume-title":"Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. (Dec","author":"Chung Junyoung","year":"2014","unstructured":"Junyoung Chung , Caglar Gulcehre , KyungHyun Cho , and Yoshua Bengio . 2014. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. (Dec . 2014 ). arXiv:1412.3555 [cs.NE] Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. 2014. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. (Dec. 2014). arXiv:1412.3555 [cs.NE]"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482315"},{"key":"e_1_3_2_2_5_1","volume-title":"International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=9z_dNsC4B5t","author":"Du Yingjun","unstructured":"Yingjun Du , Xiantong Zhen , Ling Shao , and Cees G. M. Snoek . 2021. MetaNorm: Learning to Normalize Few-Shot Batches Across Domains . In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=9z_dNsC4B5t Yingjun Du, Xiantong Zhen, Ling Shao, and Cees G. M. Snoek. 2021. MetaNorm: Learning to Normalize Few-Shot Batches Across Domains. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=9z_dNsC4B5t"},{"key":"e_1_3_2_2_6_1","volume-title":"Proceedings of the 34th International Conference on Machine Learning -","volume":"70","author":"Finn Chelsea","year":"2017","unstructured":"Chelsea Finn , Pieter Abbeel , and Sergey Levine . 2017 . Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks . In Proceedings of the 34th International Conference on Machine Learning - Volume 70 (Sydney, NSW, Australia) (ICML'17). JMLR.org, 1126--1135. Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. In Proceedings of the 34th International Conference on Machine Learning - Volume 70 (Sydney, NSW, Australia) (ICML'17). JMLR.org, 1126--1135."},{"key":"e_1_3_2_2_7_1","volume-title":"Online Meta-Learning. In Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research","volume":"1930","author":"Finn Chelsea","year":"2019","unstructured":"Chelsea Finn , Aravind Rajeswaran , Sham Kakade , and Sergey Levine . 2019 . Online Meta-Learning. In Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research , Vol. 97), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.). PMLR, 1920-- 1930 . https:\/\/proceedings.mlr. press\/v97\/finn19a.html Chelsea Finn, Aravind Rajeswaran, Sham Kakade, and Sergey Levine. 2019. Online Meta-Learning. In Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 97), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.). PMLR, 1920--1930. https:\/\/proceedings.mlr. press\/v97\/finn19a.html"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/2523813"},{"key":"e_1_3_2_2_9_1","volume-title":"Deep Residual Learning for Image Recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE. https:\/\/doi.org\/10","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. https:\/\/doi.org\/10 .1109\/cvpr.2016.90 10.1109\/cvpr.2016.90 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. https:\/\/doi.org\/10.1109\/cvpr.2016.90"},{"key":"e_1_3_2_2_10_1","volume-title":"Yee Whye Teh, and Razvan Pascanu","author":"He Xu","year":"2019","unstructured":"Xu He , Jakub Sygnowski , Alexandre Galashov , Andrei A. Rusu , Yee Whye Teh, and Razvan Pascanu . 2019 . Task Agnostic Continual Learning via Meta Learning . (June 2019). arXiv:1906.05201 [stat.ML] Xu He, Jakub Sygnowski, Alexandre Galashov, Andrei A. Rusu, Yee Whye Teh, and Razvan Pascanu. 2019. Task Agnostic Continual Learning via Meta Learning. (June 2019). arXiv:1906.05201 [stat.ML]"},{"key":"e_1_3_2_2_11_1","volume-title":"4th Lifelong Machine Learning Workshop at ICML","author":"He Xu","year":"2020","unstructured":"Xu He , Jakub Sygnowski , Alexandre Galashov , Andrei Alex Rusu , Yee Whye Teh , and Razvan Pascanu . 2020 . Task Agnostic Continual Learning via Meta Learning . In 4th Lifelong Machine Learning Workshop at ICML 2020. https: \/\/openreview.net\/forum?id=AeIzVxdJgeb Xu He, Jakub Sygnowski, Alexandre Galashov, Andrei Alex Rusu, Yee Whye Teh, and Razvan Pascanu. 2020. Task Agnostic Continual Learning via Meta Learning. In 4th Lifelong Machine Learning Workshop at ICML 2020. https: \/\/openreview.net\/forum?id=AeIzVxdJgeb"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482483"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3159652.3159690"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.2992934"},{"key":"e_1_3_2_2_16_1","volume-title":"Stock Price Prediction Using Attention-based Multi-Input LSTM. In Asian Conference on Machine Learning.","author":"Li Hao","year":"2018","unstructured":"Hao Li , Yanyan Shen , and Yanmin Zhu . 2018 . Stock Price Prediction Using Attention-based Multi-Input LSTM. In Asian Conference on Machine Learning. Hao Li, Yanyan Shen, and Yanmin Zhu. 2018. Stock Price Prediction Using Attention-based Multi-Input LSTM. In Asian Conference on Machine Learning."},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i4.20327"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330833"},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467358"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1803839115"},{"key":"e_1_3_2_2_21_1","volume-title":"Deep Online Learning Via Meta-Learning: Continual Adaptation for Model-Based RL. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id= HyxAfnA5tm","author":"Nagabandi Anusha","year":"2019","unstructured":"Anusha Nagabandi , Chelsea Finn , and Sergey Levine . 2019 . Deep Online Learning Via Meta-Learning: Continual Adaptation for Model-Based RL. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id= HyxAfnA5tm Anusha Nagabandi, Chelsea Finn, and Sergey Levine. 2019. Deep Online Learning Via Meta-Learning: Continual Adaptation for Model-Based RL. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id= HyxAfnA5tm"},{"key":"e_1_3_2_2_22_1","volume-title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization. https:\/\/doi.org\/10","author":"Qin Yao","year":"2017","unstructured":"Yao Qin , Dongjin Song , Haifeng Chen , Wei Cheng , Guofei Jiang , and Garrison W. Cottrell . 2017. A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction . In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization. https:\/\/doi.org\/10 .24963\/ijcai. 2017 \/366 10.24963\/ijcai.2017 Yao Qin, Dongjin Song, Haifeng Chen, Wei Cheng, Guofei Jiang, and Garrison W. Cottrell. 2017. A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization. https:\/\/doi.org\/10.24963\/ijcai.2017\/366"},{"key":"e_1_3_2_2_23_1","volume-title":"Lempitsky","author":"Ulyanov Dmitry","year":"2016","unstructured":"Dmitry Ulyanov , Andrea Vedaldi , and Victor S . Lempitsky . 2016 . Instance Nor-malization : The Missing Ingredient for Fast Stylization. ArXiv abs\/1607.08022 (2016). Dmitry Ulyanov, Andrea Vedaldi, and Victor S. Lempitsky. 2016. Instance Nor-malization: The Missing Ingredient for Fast Stylization. ArXiv abs\/1607.08022 (2016)."},{"key":"e_1_3_2_2_24_1","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"van der Maaten Laurens","year":"2008","unstructured":"Laurens van der Maaten and Geoffrey Hinton . 2008 . Visualizing data using t-SNE . Journal of Machine Learning Research (JMLR) 9 (2008), 2579 -- 2605 . www.jmlr. org\/papers\/v9\/vandermaaten08a.html Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of Machine Learning Research (JMLR) 9 (2008), 2579--2605. www.jmlr. org\/papers\/v9\/vandermaaten08a.html","journal-title":"Journal of Machine Learning Research (JMLR)"},{"key":"e_1_3_2_2_25_1","volume-title":"Attention is All you Need. ArXiv abs\/1706.03762","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani , Noam M. Shazeer , Niki Parmar , Jakob Uszkoreit , Llion Jones , Aidan N. Gomez , Lukasz Kaiser , and Illia Polosukhin . 2017. Attention is All you Need. ArXiv abs\/1706.03762 ( 2017 ). Ashish Vaswani, Noam M. Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is All you Need. ArXiv abs\/1706.03762 (2017)."},{"key":"e_1_3_2_2_26_1","volume-title":"HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information. ArXiv abs\/2110.13716","author":"Xu Wentao","year":"2021","unstructured":"Wentao Xu , Weiqing Liu , Lewen Wang , Yingce Xia , Jiang Bian , Jian Yin , and Tie-Yan Liu . 2021 . HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information. ArXiv abs\/2110.13716 (2021). Wentao Xu, Weiqing Liu, Lewen Wang, Yingce Xia, Jiang Bian, Jian Yin, and Tie-Yan Liu. 2021. HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information. ArXiv abs\/2110.13716 (2021)."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3450032"},{"key":"e_1_3_2_2_28_1","volume-title":"Qlib: An AI-oriented Quantitative Investment Platform. ArXiv abs\/2009.11189","author":"Yang Xiao","year":"2020","unstructured":"Xiao Yang , Weiqing Liu , Dong Zhou , Jiang Bian , and Tie-Yan Liu . 2020 . Qlib: An AI-oriented Quantitative Investment Platform. ArXiv abs\/2009.11189 (2020). Xiao Yang, Weiqing Liu, Dong Zhou, Jiang Bian, and Tie-Yan Liu. 2020. Qlib: An AI-oriented Quantitative Investment Platform. ArXiv abs\/2009.11189 (2020)."},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467297"},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539300"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482271"},{"key":"e_1_3_2_2_32_1","unstructured":"Donglin Zhan Yusheng Dai Yiwei Dong Jinghai He Zhenyi Wang and James Anderson. 2022. Meta-Adaptive Stock Movement Prediction with Two-Stage Representation Learning. In NeurIPS 2022 Workshop on Distribution Shifts: Connecting Methods and Applications. https:\/\/openreview.net\/forum?id=uf44d5H1vx  Donglin Zhan Yusheng Dai Yiwei Dong Jinghai He Zhenyi Wang and James Anderson. 2022. Meta-Adaptive Stock Movement Prediction with Two-Stage Representation Learning. In NeurIPS 2022 Workshop on Distribution Shifts: Connecting Methods and Applications. https:\/\/openreview.net\/forum?id=uf44d5H1vx"},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098117"},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401167"},{"key":"e_1_3_2_2_35_1","volume-title":"Forecasting Wavelet Transformed Time Series with Attentive Neural Networks. In 2018 IEEE International Conference on Data Mining (ICDM). IEEE. https:\/\/doi.org\/10","author":"Zhao Yi","year":"2018","unstructured":"Yi Zhao , Yanyan Shen , Yanmin Zhu , and Junjie Yao . 2018 . Forecasting Wavelet Transformed Time Series with Attentive Neural Networks. In 2018 IEEE International Conference on Data Mining (ICDM). IEEE. https:\/\/doi.org\/10 .1109\/icdm. 2018.00201 10.1109\/icdm Yi Zhao, Yanyan Shen, Yanmin Zhu, and Junjie Yao. 2018. Forecasting Wavelet Transformed Time Series with Attentive Neural Networks. In 2018 IEEE International Conference on Data Mining (ICDM). IEEE. https:\/\/doi.org\/10.1109\/icdm. 2018.00201"},{"key":"e_1_3_2_2_36_1","volume-title":"Online Convex Programming and Generalized Infini-tesimal Gradient Ascent. In International Conference on Machine Learning.","author":"Zinkevich Martin A.","year":"2003","unstructured":"Martin A. Zinkevich . 2003 . Online Convex Programming and Generalized Infini-tesimal Gradient Ascent. In International Conference on Machine Learning. Martin A. Zinkevich. 2003. Online Convex Programming and Generalized Infini-tesimal Gradient Ascent. In International Conference on Machine Learning."}],"event":{"name":"KDD '23: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Long Beach CA USA","acronym":"KDD '23","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 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3580305.3599315","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3580305.3599315","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:37:47Z","timestamp":1750178267000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3580305.3599315"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,4]]},"references-count":36,"alternative-id":["10.1145\/3580305.3599315","10.1145\/3580305"],"URL":"https:\/\/doi.org\/10.1145\/3580305.3599315","relation":{},"subject":[],"published":{"date-parts":[[2023,8,4]]},"assertion":[{"value":"2023-08-04","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}