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Knowl. Discov. Data"],"published-print":{"date-parts":[[2024,6,30]]},"abstract":"<jats:p>Domain generalization aims to design models that can effectively generalize to unseen target domains by learning from observed source domains. Domain generalization poses a significant challenge for time series data, due to varying data distributions and temporal dependencies. Existing approaches to domain generalization are not designed for time series data, which often results in suboptimal or unstable performance when confronted with diverse temporal patterns and complex data characteristics. We propose a novel approach to tackle the problem of domain generalization in time series forecasting. We focus on a scenario where time series domains share certain common attributes and exhibit no abrupt distribution shifts. Our method revolves around the incorporation of a key regularization term into an existing time series forecasting model:<jats:italic>domain discrepancy regularization<\/jats:italic>. In this way, we aim to enforce consistent performance across different domains that exhibit distinct patterns. We calibrate the regularization term by investigating the performance within individual domains and propose the<jats:italic>domain discrepancy regularization with domain difficulty awareness<\/jats:italic>. We demonstrate the effectiveness of our method on multiple datasets, including synthetic and real-world time series datasets from diverse domains such as retail, transportation, and finance. Our method is compared against traditional methods, deep learning models, and domain generalization approaches to provide comprehensive insights into its performance. In these experiments, our method showcases superior performance, surpassing both the base model and competing domain generalization models across all datasets. Furthermore, our method is highly general and can be applied to various time series models.<\/jats:p>","DOI":"10.1145\/3643035","type":"journal-article","created":{"date-parts":[[2024,1,31]],"date-time":"2024-01-31T11:58:43Z","timestamp":1706702323000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["Domain Generalization in Time Series Forecasting"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9822-9270","authenticated-orcid":false,"given":"Songgaojun","family":"Deng","sequence":"first","affiliation":[{"name":"AIRLab, University of Amsterdam, Amsterdam, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0533-4574","authenticated-orcid":false,"given":"Olivier","family":"Sprangers","sequence":"additional","affiliation":[{"name":"AIRLab, University of Amsterdam, Amsterdam, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7430-4961","authenticated-orcid":false,"given":"Ming","family":"Li","sequence":"additional","affiliation":[{"name":"AIRLab, University of Amsterdam, Amsterdam, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4722-5840","authenticated-orcid":false,"given":"Sebastian","family":"Schelter","sequence":"additional","affiliation":[{"name":"University of Amsterdam, Amsterdam, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1086-0202","authenticated-orcid":false,"given":"Maarten","family":"de Rijke","sequence":"additional","affiliation":[{"name":"University of Amsterdam, Amsterdam, The Netherlands"}]}],"member":"320","published-online":{"date-parts":[[2024,2,27]]},"reference":[{"key":"e_1_3_3_2_2","volume-title":"International Conference on Learning Representations","author":"Ahmed Faruk","year":"2021","unstructured":"Faruk Ahmed, Yoshua Bengio, Harm van Seijen, and Aaron Courville. 2021. 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In IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2477\u20132486."},{"key":"e_1_3_3_24_2","first-page":"859","volume-title":"16th ACM International Conference on Web Search and Data Mining","author":"Gou Xiaochuan","year":"2023","unstructured":"Xiaochuan Gou and Xiangliang Zhang. 2023. Telecommunication traffic forecasting via multi-task learning. In 16th ACM International Conference on Web Search and Data Mining. 859\u2013867."},{"key":"e_1_3_3_25_2","article-title":"In search of lost domain generalization","author":"Gulrajani Ishaan","year":"2020","unstructured":"Ishaan Gulrajani and David Lopez-Paz. 2020. In search of lost domain generalization. arXiv preprint arXiv:2007.01434 (2020).","journal-title":"arXiv preprint arXiv:2007.01434"},{"issue":"8","key":"e_1_3_3_26_2","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter Sepp","year":"1997","unstructured":"Sepp Hochreiter and J\u00fcrgen Schmidhuber. 1997. Long short-term memory. Neural Computat. 9, 8 (1997), 1735\u20131780.","journal-title":"Neural Computat."},{"key":"e_1_3_3_27_2","first-page":"7806","volume-title":"International Conference on Robotics and Automation (ICRA\u201922)","author":"Hu Yeping","year":"2022","unstructured":"Yeping Hu, Xiaogang Jia, Masayoshi Tomizuka, and Wei Zhan. 2022. Causal-based time series domain generalization for vehicle intention prediction. In International Conference on Robotics and Automation (ICRA\u201922). IEEE, 7806\u20137813."},{"key":"e_1_3_3_28_2","first-page":"850","volume-title":"16th ACM International Conference on Web Search and Data Mining","author":"Huynh Thanh Trung","year":"2023","unstructured":"Thanh Trung Huynh, Minh Hieu Nguyen, Thanh Tam Nguyen, Phi Le Nguyen, Matthias Weidlich, Quoc Viet Hung Nguyen, and Karl Aberer. 2023. Efficient integration of multi-order dynamics and internal dynamics in stock movement prediction. In 16th ACM International Conference on Web Search and Data Mining. 850\u2013858."},{"key":"e_1_3_3_29_2","volume-title":"Forecasting: Principles and Practice","author":"Hyndman Rob J.","year":"2018","unstructured":"Rob J. Hyndman and George Athanasopoulos. 2018. Forecasting: Principles and Practice. OTexts."},{"key":"e_1_3_3_30_2","first-page":"10280","volume-title":"International Conference on Machine Learning","author":"Jin Xiaoyong","year":"2022","unstructured":"Xiaoyong Jin, Youngsuk Park, Danielle Maddix, Hao Wang, and Yuyang Wang. 2022. Domain adaptation for time series forecasting via attention sharing. In International Conference on Machine Learning. PMLR, 10280\u201310297."},{"key":"e_1_3_3_31_2","article-title":"LightGBM: A highly efficient gradient boosting decision tree","volume":"30","author":"Ke Guolin","year":"2017","unstructured":"Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. 2017. LightGBM: A highly efficient gradient boosting decision tree. Adv. Neural Inf. Process. Syst. 30.","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"1","key":"e_1_3_3_32_2","first-page":"307","article-title":"Financial time series forecasting using support vector machines","volume":"55","author":"Kim Kyoung-jae","year":"2003","unstructured":"Kyoung-jae Kim. 2003. Financial time series forecasting using support vector machines. Neurocomputing 55, 1-2 (2003), 307\u2013319.","journal-title":"Neurocomputing"},{"key":"e_1_3_3_33_2","volume-title":"International Conference on Learning Representations","volume":"5","author":"Kingma Durk","year":"2015","unstructured":"Durk Kingma and J. Ba Adam. 2015. Adam: A method for stochastic optimization. In International Conference on Learning Representations, Vol. 5."},{"key":"e_1_3_3_34_2","volume-title":"Applied Regression: An Introduction","author":"Lewis-Beck Colin","year":"2015","unstructured":"Colin Lewis-Beck and Michael Lewis-Beck. 2015. Applied Regression: An Introduction. Vol. 22. Sage Publications."},{"key":"e_1_3_3_35_2","volume-title":"AAAI Conference on Artificial Intelligence","volume":"32","author":"Li Da","year":"2018","unstructured":"Da Li, Yongxin Yang, Yi-Zhe Song, and Timothy Hospedales. 2018. Learning to generalize: Meta-learning for domain generalization. In AAAI Conference on Artificial Intelligence, Vol. 32."},{"key":"e_1_3_3_36_2","first-page":"5400","volume-title":"IEEE Conference on Computer Vision and Pattern Recognition","author":"Li Haoliang","year":"2018","unstructured":"Haoliang Li, Sinno Jialin Pan, Shiqi Wang, and Alex C. Kot. 2018. Domain generalization with adversarial feature learning. In IEEE Conference on Computer Vision and Pattern Recognition. 5400\u20135409."},{"key":"e_1_3_3_37_2","article-title":"Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting","volume":"32","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. Adv. Neural Inf. Process. Syst. 32 (2019).","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"e_1_3_3_38_2","first-page":"3915","volume-title":"International Conference on Machine Learning","author":"Li Yiying","year":"2019","unstructured":"Yiying Li, Yongxin Yang, Wei Zhou, and Timothy Hospedales. 2019. Feature-critic networks for heterogeneous domain generalization. In International Conference on Machine Learning. PMLR, 3915\u20133924."},{"issue":"2194","key":"e_1_3_3_39_2","article-title":"Time-series forecasting with deep learning: A survey","volume":"379","author":"Lim Bryan","year":"2021","unstructured":"Bryan Lim and Stefan Zohren. 2021. Time-series forecasting with deep learning: A survey. Philos. Trans. Roy. Societ. A 379, 2194 (2021), 20200209.","journal-title":"Philos. Trans. Roy. Societ. A"},{"key":"e_1_3_3_40_2","volume-title":"11th International Conference on Learning Representations","author":"Lu Wang","year":"2022","unstructured":"Wang Lu, Jindong Wang, Xinwei Sun, Yiqiang Chen, and Xing Xie. 2022. Out-of-distribution representation learning for time series classification. In 11th International Conference on Learning Representations."},{"issue":"3","key":"e_1_3_3_41_2","doi-asserted-by":"crossref","first-page":"2019","DOI":"10.1007\/s10462-017-9593-z","article-title":"A methodology for applying k-nearest neighbor to time series forecasting","volume":"52","author":"Mart\u00ednez Francisco","year":"2019","unstructured":"Francisco Mart\u00ednez, Mar\u00eda Pilar Fr\u00edas, Mar\u00eda Dolores P\u00e9rez, and Antonio Jes\u00fas Rivera. 2019. A methodology for applying k-nearest neighbor to time series forecasting. Artif. Intell. Rev. 52, 3 (2019), 2019\u20132037.","journal-title":"Artif. Intell. Rev."},{"key":"e_1_3_3_42_2","volume-title":"Corporaci\u00f3n Favorita Grocery Sales Forecasting Kaggle Competition","author":"Calero Augusto Steves Mendoza","year":"2018","unstructured":"Augusto Steves Mendoza Calero. 2018. Corporaci\u00f3n Favorita Grocery Sales Forecasting Kaggle Competition. Master\u2019s thesis. Universidad Internacional de Andaluc\u00eda."},{"key":"e_1_3_3_43_2","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1007\/978-3-540-74048-3_4","volume-title":"Information Retrieval for Music and Motion","author":"M\u00fcller Meinard","year":"2007","unstructured":"Meinard M\u00fcller. 2007. Dynamic time warping. In Information Retrieval for Music and Motion. Springer, 69\u201384."},{"key":"e_1_3_3_44_2","first-page":"19198","article-title":"Training for the future: A simple gradient interpolation loss to generalize along time","volume":"34","author":"Nasery Anshul","year":"2021","unstructured":"Anshul Nasery, Soumyadeep Thakur, Vihari Piratla, Abir De, and Sunita Sarawagi. 2021. Training for the future: A simple gradient interpolation loss to generalize along time. Adv. Neural Inf. Process. Syst. 34 (2021), 19198\u201319209.","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"e_1_3_3_45_2","article-title":"WaveNet: A generative model for raw audio","author":"Oord Aaron van den","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).","journal-title":"arXiv preprint arXiv:1609.03499"},{"key":"e_1_3_3_46_2","article-title":"PyTorch: An imperative style, high-performance deep learning library","volume":"32","author":"Paszke Adam","year":"2019","unstructured":"Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas K\u00f6pf, Edward Yang, Zach DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019. PyTorch: An imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. 32 (2019).","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"e_1_3_3_47_2","first-page":"12556","volume-title":"IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Qiao Fengchun","year":"2020","unstructured":"Fengchun Qiao, Long Zhao, and Xi Peng. 2020. Learning to learn single domain generalization. In IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 12556\u201312565."},{"key":"e_1_3_3_48_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2022.3154000","article-title":"Conditional contrastive domain generalization for fault diagnosis","volume":"71","author":"Ragab Mohamed","year":"2022","unstructured":"Mohamed Ragab, Zhenghua Chen, Wenyu Zhang, Emadeldeen Eldele, Min Wu, Chee-Keong Kwoh, and Xiaoli Li. 2022. Conditional contrastive domain generalization for fault diagnosis. IEEE Trans. Instrum. Measur. 71 (2022), 1\u201312.","journal-title":"IEEE Trans. Instrum. Measur."},{"key":"e_1_3_3_49_2","doi-asserted-by":"crossref","DOI":"10.1002\/9780470382806","volume-title":"Modern Regression Methods","author":"Ryan Thomas P.","year":"2008","unstructured":"Thomas P. Ryan. 2008. Modern Regression Methods. Vol. 655. John Wiley & Sons."},{"key":"e_1_3_3_50_2","article-title":"Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization","author":"Sagawa Shiori","year":"2019","unstructured":"Shiori Sagawa, Pang Wei Koh, Tatsunori B. Hashimoto, and Percy Liang. 2019. Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization. arXiv preprint arXiv:1911.08731 (2019).","journal-title":"arXiv preprint arXiv:1911.08731"},{"issue":"3","key":"e_1_3_3_51_2","doi-asserted-by":"crossref","first-page":"1181","DOI":"10.1016\/j.ijforecast.2019.07.001","article-title":"DeepAR: Probabilistic forecasting with autoregressive recurrent networks","volume":"36","author":"Salinas David","year":"2020","unstructured":"David Salinas, Valentin Flunkert, Jan Gasthaus, and Tim Januschowski. 2020. DeepAR: Probabilistic forecasting with autoregressive recurrent networks. Int. J. Forecast. 36, 3 (2020), 1181\u20131191.","journal-title":"Int. J. Forecast."},{"key":"e_1_3_3_52_2","first-page":"3076","volume-title":"International Conference on Machine Learning","author":"Shalit Uri","year":"2017","unstructured":"Uri Shalit, Fredrik D. Johansson, and David Sontag. 2017. Estimating individual treatment effect: Generalization bounds and algorithms. In International Conference on Machine Learning. JMLR. org, 3076\u20133085."},{"key":"e_1_3_3_53_2","article-title":"Generalizing across domains via cross-gradient training","author":"Shankar Shiv","year":"2018","unstructured":"Shiv Shankar, Vihari Piratla, Soumen Chakrabarti, Siddhartha Chaudhuri, Preethi Jyothi, and Sunita Sarawagi. 2018. Generalizing across domains via cross-gradient training. arXiv preprint arXiv:1804.10745 (2018).","journal-title":"arXiv preprint arXiv:1804.10745"},{"key":"e_1_3_3_54_2","article-title":"Gradient matching for domain generalization","author":"Shi Yuge","year":"2021","unstructured":"Yuge Shi, Jeffrey Seely, Philip H. S. Torr, N. Siddharth, Awni Hannun, Nicolas Usunier, and Gabriel Synnaeve. 2021. Gradient matching for domain generalization. arXiv preprint arXiv:2104.09937 (2021).","journal-title":"arXiv preprint arXiv:2104.09937"},{"issue":"1","key":"e_1_3_3_55_2","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.ijforecast.2021.11.011","article-title":"Parameter-efficient deep probabilistic forecasting","volume":"39","author":"Sprangers Olivier","year":"2023","unstructured":"Olivier Sprangers, Sebastian Schelter, and Maarten de Rijke. 2023. Parameter-efficient deep probabilistic forecasting. Int. J. Forecast. 39, 1 (2023), 332\u2013345.","journal-title":"Int. J. Forecast."},{"issue":"1","key":"e_1_3_3_56_2","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression shrinkage and selection via the lasso","volume":"58","author":"Tibshirani Robert","year":"1996","unstructured":"Robert Tibshirani. 1996. Regression shrinkage and selection via the lasso. J. Roy. Stat. Societ.: Series B (Methodol.) 58, 1 (1996), 267\u2013288.","journal-title":"J. Roy. Stat. Societ.: Series B (Methodol.)"},{"key":"e_1_3_3_57_2","first-page":"23","volume-title":"IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS\u201917)","author":"Tobin Josh","year":"2017","unstructured":"Josh Tobin, Rachel Fong, Alex Ray, Jonas Schneider, Wojciech Zaremba, and Pieter Abbeel. 2017. Domain randomization for transferring deep neural networks from simulation to the real world. In IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS\u201917). IEEE, 23\u201330."},{"key":"e_1_3_3_58_2","volume-title":"The Nature of Statistical Learning Theory","author":"Vapnik Vladimir","year":"1999","unstructured":"Vladimir Vapnik. 1999. The Nature of Statistical Learning Theory. Springer Science & Business Media."},{"key":"e_1_3_3_59_2","article-title":"Attention is all you need","volume":"30","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. Adv. Neural Inf. Process. Syst. 30 (2017).","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"e_1_3_3_60_2","article-title":"Generalizing to unseen domains via adversarial data augmentation","volume":"31","author":"Volpi Riccardo","year":"2018","unstructured":"Riccardo Volpi, Hongseok Namkoong, Ozan Sener, John C. Duchi, Vittorio Murino, and Silvio Savarese. 2018. Generalizing to unseen domains via adversarial data augmentation. Adv. Neural Inf. Process. Syst. 31 (2018).","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"e_1_3_3_61_2","doi-asserted-by":"crossref","first-page":"616","DOI":"10.1145\/3437963.3441731","volume-title":"14th ACM International Conference on Web Search and Data Mining","author":"Wang Chunyang","year":"2021","unstructured":"Chunyang Wang, Yanmin Zhu, Tianzi Zang, Haobing Liu, and Jiadi Yu. 2021. Modeling inter-station relationships with attentive temporal graph convolutional network for air quality prediction. In 14th ACM International Conference on Web Search and Data Mining. 616\u2013634."},{"issue":"1","key":"e_1_3_3_62_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3360309","article-title":"Transfer learning with dynamic distribution adaptation","volume":"11","author":"Wang Jindong","year":"2020","unstructured":"Jindong Wang, Yiqiang Chen, Wenjie Feng, Han Yu, Meiyu Huang, and Qiang Yang. 2020. Transfer learning with dynamic distribution adaptation. ACM Trans. Intell. Syst. Technol. 11, 1 (2020), 1\u201325.","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"e_1_3_3_63_2","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1145\/3240508.3240512","volume-title":"26th ACM International Conference on Multimedia","author":"Wang Jindong","year":"2018","unstructured":"Jindong Wang, Wenjie Feng, Yiqiang Chen, Han Yu, Meiyu Huang, and Philip S. Yu. 2018. Visual domain adaptation with manifold embedded distribution alignment. In 26th ACM International Conference on Multimedia. 402\u2013410."},{"key":"e_1_3_3_64_2","unstructured":"Jindong Wang Cuiling Lan Chang Liu Yidong Ouyang Tao Qin Wang Lu Yiqiang Chen Wenjun Zeng and Philip Yu. 2023. Generalizing to unseen domains: A survey on domain generalization. IEEE Transactions on Knowledge and Data Engineering 35 8 (2023) 8052\u20138072."},{"issue":"1","key":"e_1_3_3_65_2","first-page":"4","article-title":"A comprehensive survey on graph neural networks","volume":"32","author":"Wu Zonghan","year":"2020","unstructured":"Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and Philip S. Yu. 2020. A comprehensive survey on graph neural networks. IEEE Trans. Neural Netw. Learn. Syst. 32, 1 (2020), 4\u201324.","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"e_1_3_3_66_2","first-page":"2100","volume-title":"IEEE\/CVF International Conference on Computer Vision","author":"Yue Xiangyu","year":"2019","unstructured":"Xiangyu Yue, Yang Zhang, Sicheng Zhao, Alberto Sangiovanni-Vincentelli, Kurt Keutzer, and Boqing Gong. 2019. Domain randomization and pyramid consistency: Simulation-to-real generalization without accessing target domain data. In IEEE\/CVF International Conference on Computer Vision. 2100\u20132110."},{"key":"e_1_3_3_67_2","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1145\/3450439.3451878","volume-title":"Conference on Health, Inference, and Learning","author":"Zhang Haoran","year":"2021","unstructured":"Haoran Zhang, Natalie Dullerud, Laleh Seyyed-Kalantari, Quaid Morris, Shalmali Joshi, and Marzyeh Ghassemi. 2021. An empirical framework for domain generalization in clinical settings. In Conference on Health, Inference, and Learning. 279\u2013290."},{"key":"e_1_3_3_68_2","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.physa.2017.02.072","article-title":"Multidimensional k-nearest neighbor model based on EEMD for financial time series forecasting","volume":"477","author":"Zhang Ningning","year":"2017","unstructured":"Ningning Zhang, Aijing Lin, and Pengjian Shang. 2017. Multidimensional k-nearest neighbor model based on EEMD for financial time series forecasting. Phys. A: Stat. Mechan. Applic. 477 (2017), 161\u2013173.","journal-title":"Phys. A: Stat. Mechan. Applic."},{"key":"e_1_3_3_69_2","unstructured":"Shichao Zhang and Jiaye Li. 2023. KNN classification with One-step computation. IEEE Transactions on Knowledge and Data Engineering 35 3 (2023) 2711\u20132723."},{"key":"e_1_3_3_70_2","unstructured":"Shichao Zhang Jiaye Li and Yangding Li. 2023. Reachable distance function for KNN classification. IEEE Transactions on Knowledge and Data Engineering 35 7 (2023) 7382\u20137396."},{"key":"e_1_3_3_71_2","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.neucom.2022.06.082","article-title":"Hyper-class representation of data","volume":"503","author":"Zhang Shichao","year":"2022","unstructured":"Shichao Zhang, Jiaye Li, Wenzhen Zhang, and Yongsong Qin. 2022. Hyper-class representation of data. Neurocomputing 503 (2022), 200\u2013218.","journal-title":"Neurocomputing"},{"issue":"5","key":"e_1_3_3_72_2","doi-asserted-by":"crossref","first-page":"1774","DOI":"10.1109\/TNNLS.2017.2673241","article-title":"Efficient kNN classification with different numbers of nearest neighbors","volume":"29","author":"Zhang Shichao","year":"2017","unstructured":"Shichao Zhang, Xuelong Li, Ming Zong, Xiaofeng Zhu, and Ruili Wang. 2017. Efficient kNN classification with different numbers of nearest neighbors. IEEE Trans. Neural Netw. Learn. Syst. 29, 5 (2017), 1774\u20131785.","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"e_1_3_3_73_2","first-page":"2149","volume-title":"26th International Conference on Pattern Recognition (ICPR\u201922)","author":"Zhang Wenyu","year":"2022","unstructured":"Wenyu Zhang, Mohamed Ragab, and Chuan-Sheng Foo. 2022. Domain generalization via selective consistency regularization for time series classification. In 26th International Conference on Pattern Recognition (ICPR\u201922). 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