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KDD 2025将在2025年8月3号到7号在加拿大多伦多举行，本文总结了KDD 2025(August Cycle)有关时间序列（Time Series）相关文章，共计11篇，其中1-10为Research Track，11为ADS Track。如有疏漏，欢迎补充！
时间序列Topic：预测，异常检测，测试时适应等。
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Research
1 IN-Flow: Instance Normalization Flow for Non-stationary Time Series Forecasting.
链接：https://dl.acm.org/doi/10.1145/3690624.3709260
作者：Wei Fan, Shun Zheng, Pengyang Wang, Rui Xie, Kun Yi, Qi Zhang, Jiang Bian, Yanjie Fu
关键词：预测，分布偏移，归一化流
2 Quantum Time-index Models with Reservoir for Time Series Forecasting
链接：https://dl.acm.org/doi/10.1145/3690624.3709228
作者：Wenbo Qiao, Jiaming Zhao, Peng Zhang
关键词：预测，时间索引，量子模型
3 DUET: Dual Clustering Enhanced Multivariate Time Series Forecasting
链接：https://dl.acm.org/doi/10.1145/3690624.3709325
代码：https://github.com/decisionintelligence/DUET
作者： Xiangfei Qiu, Xingjian Wu, Yan Lin, Chenjuan Guo, Jilin Hu, Bin Yang
关键词：预测，双向聚类
4 ST-MTM: Masked Time Series Modeling with Seasonal-Trend Decomposition for Time Series Forecasting
链接：https://dl.acm.org/doi/10.1145/3690624.3709254
作者：Hyunwoo Seo, Chiehyeon Lim
关键词：预测，掩码时间序列建模，季节性 - 趋势分解的
5 Probabilistic Hypergraph Recurrent Neural Networks for Time-series Forecasting
链接：https://dl.acm.org/doi/10.1145/3690624.3709202
作者：Hongjie Chen, Ryan A. Rossi, Sungchul Kim, Kanak Mahadik, Hoda Eldardir
关键词：概率预测，超图，循环神经网络
6 Proactive Model Adaptation Against Concept Drift for Online Time Series Forecasting
链接：https://dl.acm.org/doi/10.1145/3690624.3709210
代码：https://github.com/SJTU-DMTai/OnlineTSF
作者： Lifan Zhao, Yanyan Shen
关键词：预测，在线学习，概念漂移
7 Learning Universal Multi-level Market Irrationality Factors to Improve Stock Return Forecasting
链接：https://dl.acm.org/doi/10.1145/3690624.3709328
代码：https://github.com/lIcIIl/UMI
作者：Chen Yang, Jingyuan Wang, Xiaohan Jiang, Junjie Wu
关键词：股票收益预测，市场非理性，深度学习，自监督学习
8 TSINR: Capturing Temporal Continuity via Implicit Neural Representations for Time Series Anomaly Detection
链接：https://dl.acm.org/doi/10.1145/3690624.3709266
代码：https://github.com/Leanna97/TSINR
作者：Mengxuan Li, Ke Liu, Hongyang Chen, Jiajun Bu, Hongwei Wang, Haishuai Wang
关键词：异常检测，隐藏神经表示，无监督学习
9 Noise-Resilient Point-wise Anomaly Detection in Time Series Using Weak Segment Labels
链接：https://dl.acm.org/doi/10.1145/3690624.3709257
代码：https://github.com/UCSC-REAL/Nrdetector
作者：Yaxuan Wang, Hao Cheng, Jing Xiong, Qingsong Wen, Han Jia, Ruixuan Song, Liyuan Zhang, Zhaowei Zhu, Yang Liu
关键词：异常检测，正样本和无标签学习
10 Augmented Contrastive Clustering with Uncertainty-Aware Prototyping for Time Series Test Time Adaptation
链接：https://dl.acm.org/doi/10.1145/3690624.3709239
代码：https://github.com/Tokenmw/ACCUP-main
作者：Peiliang Gong, Mohamed Ragab, Min Wu, Zhenghua Chen, Yongyi Su, Xiaoli Li, Daoqiang Zhang
关键词：测试时适应，原型学习
ADS
11 Multi-period Learning for Financial Time Series Forecasting
链接：https://dl.acm.org/doi/10.1145/3690624.3709422
代码：https://github.com/Meteor-Stars/MLF
作者：u Zhang, Zhengang Huang, Yunzhi Wu, Xun Lu, Erpeng Qi, Yunkai Chen, Zhongya Xue, Qitong Wang, Peng Wang, Wei Wang
关键词：金融时间序列预测，多周期学习
🌟【紧跟前沿】“时空探索之旅”与你一起探索时空奥秘！🚀
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