Publications
[Research Summary]
CORE A*/CCF-A (40 papers): NeurIPS x4, ICLR x7, ICML x3, KDD x8, WWW x2, SIGMOD x1, VLDB x2, ICDE x2, AAAI x4, IJCAI x4, TKDE x1, TPAMI x1, PIEEE x1.
Highlights: Oral/Spotlight Paper (NeurIPS'22, ICML'24, ICLR'24), Most Influential Paper (IJCAI'21, IJCAI'23), IAAI Deployed Application Award (AAAI'23)
Current R&D Area: AI for Time Series (AI4TS), AI for Education (AI4EDU), Decision Intelligence, LLM & GenAI in EdTech, Cloud Computing/AIOps, Smart Energy, and E-Commerce/BI Industries, via AI Technologies (like LLM, AI Agent, GenAI, Transformer, SSL, GNN, XAI, AutoML, etc.). My publications can also be found at Google Scholar.
[Conference Papers]
AI, ML, DM: Time Series, Education, Forecasting, Anomaly Detection, LLM, AI Agent, RCA, AIOps, GNN, SSL, XAI, etc. (* indicates the corresponding author)
[C76] [CIKM'24] Zefan Wang, Zichuan Liu, Yingying Zhang, Aoxiao Zhong, Jihong Wang, Fengbin Yin, Lunting Fan, Lingfei Wu, Qingsong Wen, "RCAgent: Cloud Root Cause Analysis by Autonomous Agents with Tool-Augmented Large Language Models,” in the 33rd ACM International Conference on Information and Knowledge Management (CIKM 2024), Boise, USA, Oct. 2024. [arXiv]
[C75] [CIKM'24] Shubao Zhao, Ming Jin, Zhaoxiang Hou, Chengyi Yang, Zengxiang Li, Qingsong Wen, Yi Wang, "HiMTM: Hierarchical Multi-Scale Masked Time Series Modeling with Self-Distillation for Long-Term Forecasting,” in the 33rd ACM International Conference on Information and Knowledge Management (CIKM 2024), Boise, USA, Oct. 2024. [arXiv]
[C74] [CIKM'24] Chaoli Zhang, Yingying Zhang, Lanshu Peng, Qingsong Wen, Yiyuan Yang, Chongjiong Fan, Minqi Jiang, Lunting Fan, Liang Sun, "Advancing Multivariate Time Series Anomaly Detection: A Comprehensive Benchmark with Real-World Data from Alibaba Cloud,” in the 33rd ACM International Conference on Information and Knowledge Management (CIKM 2024), Boise, USA, Oct. 2024.
[C73] [KDD'24] Yuxuan Liang, Haomin Wen, Yuqi Nie, Yushan Jiang, Ming Jin, Dongjin Song, Shirui Pan, Qingsong Wen*, "Foundation Models for Time Series Analysis: A Tutorial and Survey", in 30th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD 2024), Barcelona, Spain, Aug. 2024. [arXiv]
[C72] [KDD'24] Aoxiao Zhong, Dengyao Mo, Guiyang Liu, Jinbu Liu, Qingda Lu, Qi Zhou, Jiesheng Wu, Quanzheng Li, Qingsong Wen, "LogParser-LLM: Advancing Efficient Log Parsing with Large Language Models", in 30th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD 2024), Barcelona, Spain, Aug. 2024.
[C71] [KDD'24] Feiyi Chen, Yingying Zhang, Lunting Fan, Yuxuan Liang, Guansong Pang, Qingsong Wen, Shuiguang Deng, "Cluster-Wide Task Slowdown Detection in Cloud Systems", in 30th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD 2024), Barcelona, Spain, Aug. 2024.
[C70] [KDD'24] Zheng Dong, Renhe Jiang, Haotian Gao, Hangchen Liu, Jinliang Deng, Qingsong Wen, Xuan Song, "Heterogeneity-Informed Meta-Parameter Learning for Spatiotemporal Time Series Forecasting", in 30th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD 2024), Barcelona, Spain, Aug. 2024. [arXiv] [code]
[C69] [ICML'24] Ming Jin, Yifan Zhang, Wei Chen, Kexin Zhang, Yuxuan Liang*, Bin Yang, Jindong Wang, Shirui Pan, Qingsong Wen*, "Position: What Can Large Language Models Tell Us about Time Series Analysis," in the International Conference on Machine Learning (ICML 2024), Vienna, Austria, July 21-27, 2024. [arXiv]
[C68] [ICML'24] Shikai Fang, Qingsong Wen*, Yingtao Luo, Shandian Zhe, Liang Sun, "BayOTIDE: Bayesian Online Multivariate Time Series Imputation with Functional Decomposition, " in the International Conference on Machine Learning (ICML 2024), Vienna, Austria, July 21-27, 2024. (ICML Spotlight, Top 3.5%) [arXiv] [code]
[C67] [ICLR'24] Ming Jin, Shiyu Wang, Lintao Ma, Zhixuan Chu, James Y. Zhang, Xiaoming Shi, Pin-Yu Chen, Yuxuan Liang, Yuan-Fang Li, Shirui Pan*, Qingsong Wen*, "Time-LLM: Time Series Forecasting by Reprogramming Large Language Models," in the Twelfth International Conference on Learning Representations (ICLR 2024), Vienna, Austria, May 2024. [arXiv] [code]
[C66] [ICLR'24] Hao Cheng, Qingsong Wen*, Yang Liu, Liang Sun, "RobustTSF: Towards Theory and Design of Robust Time Series Forecasting with Anomalies," in the Twelfth International Conference on Learning Representations (ICLR 2024), Vienna, Austria, May 2024. [arXiv] [code]
[C65] [ICLR'24] Zichuan Liu, Yingying Zhang, Tianchun Wang, Zefan Wang, Dongsheng Luo, Mengnan Du, Min Wu, Yi Wang, Chunlin Chen, Lunting Fan, Qingsong Wen*, "Explaining Time Series via Contrastive and Locally Sparse Perturbations," in the Twelfth International Conference on Learning Representations (ICLR 2024), Vienna, Austria, May 2024. [arXiv] [code]
[C64] [ICLR'24] Xue Wang, Tian Zhou, Qingsong Wen, Jinyang Gao, Bolin Ding, Rong Jin, "CARD: Channel Aligned Robust Blend Transformer for Time Series Forecasting," in the Twelfth International Conference on Learning Representations (ICLR 2024), Vienna, Austria, May 2024. [arXiv] [code]
[C63] [ICLR'24] Peng Chen, Yingying Zhang, Yunyao Cheng, Yang Shu, Yihang Wang, Qingsong Wen, Bin Yang, Chenjuan Guo, "Pathformer: Multi-scale Transformers with Adaptive Pathways for Time Series Forecasting," in the Twelfth International Conference on Learning Representations (ICLR 2024), Vienna, Austria, May 2024. [arXiv] [code]
[C62] [ICLR'24] Siqiao Xue, Xiaoming Shi, Zhixuan Chu, Yan Wang, Hongyan Hao, Fan Zhou, Caigao Jiang, Chen Pan, James Y. Zhang, Qingsong Wen, Jun Zhou, Hongyuan Mei, "EasyTPP: Towards Open Benchmarking Temporal Point Processes," in the Twelfth International Conference on Learning Representations (ICLR 2024), Vienna, Austria, May 2024. [arXiv] [code]
[C61] [ICLR'24] Xin Zheng, Dongjin Song, Qingsong Wen, Bo Du, Shirui Pan, "Online GNN Evaluation Under Test-time Graph Distribution Shifts," in the Twelfth International Conference on Learning Representations (ICLR 2024), Vienna, Austria, May 2024. (ICLR Spotlight, Top 5%) [arXiv] [code]
[C60] [WWW'24] Feiyi Chen, Zhen Qin, Mengchu Zhou, Yingying Zhang, Shuiguang Deng, Lunting Fan, Guansong Pang, Qingsong Wen, "LARA: A Light and Anti-overfitting Retraining Approach for Unsupervised Time Series Anomaly Detection," In the ACM Web Conference 2024 (WWW ’24), Singapore, May 2024. [arXiv]
[C59] [WWW'24] Yibo Yan, Haomin Wen, Siru Zhong, Wei Chen, Haodong Chen, Qingsong Wen, Roger Zimmermann, Yuxuan Liang, "UrbanCLIP: Learning Text-enhanced Urban Region Profiling with Contrastive Language-Image Pretraining from the Web," In the ACM Web Conference 2024 (WWW ’24), Singapore, May 2024. [arXiv]
[C58] [VLDB'24] Yunyao Cheng, Peng Chen, Chenjuan Guo, Kai Zhao, Qingsong Wen, Bin Yang, Christian S. Jensen, "Weakly Guided Adaptation for Robust Time Series Forecasting," in the 50th International Conference on Very Large Data Bases (VLDB 2024), Guangzhou, China, Aug. 2024. [paper]
[C57] [ICDE'24] Feiyi Chen, Yingying zhang, Zhen Qin, Lunting Fan, Renhe Jiang, Yuxuan Liang, Qingsong Wen, Shuiguang Deng, "Learning Multi-Pattern Normalities in the Frequency Domain for Efficient Time Series Anomaly Detection," in IEEE 40th International Conference on Data Engineering (ICDE 2024), Utrecht, Netherlands, May 2024. [arXiv]
[C56] [IJCAI'24] Huaiwu Zhang, Yutong Xia, Siru Zhong, Kun Wang, Zekun Tong, Qingsong Wen, Roger Zimmermann, Yuxuan Liang, "Predicting Carpark Availability in Singapore with Cross-Domain Data: A New Dataset and A Data-Driven Approach", in the 33rd International Joint Conference on Artificial Intelligence (IJCAI 2023), Jeju Island, South Korea, Aug. 2024. [arXiv]
[C55] [SDM'24] Qiang Li, Yiqiao Sun, Linsey Pang, Liang Sun, Qingsong Wen, "Stable Synthetic Control with Anomaly Detection for Causal Inference," in Proc. SIAM International Conference on Data Mining (SDM 2024), Houston, USA, April 2024.
[C54] [ICASSP'24] Zhiqiang Zhou, Linxiao Yang, Qingsong Wen, Liang Sun, "RobustTSVar: A Robust Time Series Variance Estimation Algorithm," in Proc. IEEE 49th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2024), Seoul, Korea, April 2024.
[C53] [ICASSP'24] Kexin Zhang, Qingsong Wen*, Chaoli Zhang, Liang Sun, Yong Liu, "Skip-Step Contrastive Predictive Coding for Time Series Anomaly Detection," in Proc. IEEE 49th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2024), Seoul, Korea, April 2024.
[C52] [NeurIPS'23] Yifan Zhang, Qingsong Wen*, Xue Wang, Weiqi Chen, Liang Sun, Zhang Zhang, Liang Wang, Rong Jin, Tieniu Tan, "OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensembling," in Proc. 37th Annual Conference on Neural Information Processing Systems (NeurIPS 2023), New Orleans, USA, Dec. 2023. [arXiv] [code]
[C51] [NeurIPS'23] Minqi Jiang, Chaochuan Hou, Ao Zheng, Songqiao Han, Hailiang Huang, Qingsong Wen, Xiyang Hu, Yue Zhao, "ADGym: Design Choices for Deep Anomaly Detection," in Proc. 37th Annual Conference on Neural Information Processing Systems (NeurIPS 2023), New Orleans, USA, Dec. 2023. [arXiv] [code]
[C50] [KDD'23] Yiyuan Yang, Chaoli Zhang, Tian Zhou, Qingsong Wen*, Liang Sun, "DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection", in Proc. 29th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD 2023), Long Beach, USA, Aug. 2023. (Research Track, Acceptance Rate 313/1416=22.10%) [arXiv] [slides] [code]
[C49] [VLDB'23] Zhicheng Pan, Yihang Wang, Yingying Zhang, Sean Bin Yang, Peng Chen, Yunyao Cheng, Chenjuan Guo, Qingsong Wen, Xiduo Tian, Yunliang Dou, Zhiqiang Zhou, Chengcheng Yang, Aoying Zhou, Bin Yang, "MagicScaler: Uncertainty-aware, Predictive Autoscaling", in the 49th International Conference on Very Large Data Bases (VLDB 2023), Vancouver, Canada, Aug. 2023. [paper]
[C48] [IJCAI'23] Qingsong Wen, Tian Zhou, Chaoli Zhang, Weiqi Chen, Ziqing Ma, Junchi Yan, Liang Sun, "Transformers in Time Series: A Survey", in the 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, China, Aug. 2023. [arXiv] [Website]. Selected by Paper Digest into Most Influential IJCAI Papers (Version: 2023-09), Rank 1st (1/700+ IJCAI’23 papers).
[C47] [SIGSPATIAL’23] Haomin Wen, Youfang Lin, Yutong Xia, Huaiyu Wan, Qingsong Wen, Roger Zimmermann, and Yuxuan Liang, "DiffSTG: Probabilistic spatio-temporal graph forecasting with denoising diffusion models", In Proc. 31st International Conference on Advances in Geographic Information Systems (SIGSPATIAL 2023), Hamburg, Germany, Nov. 2023. [arXiv] [code]
[C46] [ICML'23 WS] Hao Cheng, Qingsong Wen, Yang Liu, Liang Sun, "Towards an Efficient Algorithm for Time Series Forecasting with Anomalies," in ICML 2023 Workshop on Data-centric Machine Learning Research, Hawaii, USA, Jul. 2023. [paper]
[C45] [ICML'23 WS] Yi-Fan Zhang, Qingsong Wen, Xue Wang, Weiqi Chen, Liang Sun, Zhang Zhang, Liang Wang, Rong Jin, Tieniu Tan, "Enhancing Time Series Forecasting Models under Concept Drift by Data-centric Online Ensembling," in ICML 2023 Workshop on Data-centric Machine Learning Research, Hawaii, USA, Jul. 2023.
[C44] [ICASSP'23] Qingsong Wen, Linxiao Yang, Liang Sun, "Robust Dominant Periodicity Detection for Time Series with Missing Data", in Proc. IEEE 48th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2023), Rhodes Island, Greece, June 2023. [arXiv]
[C43] [ICASSP'23] Hengbo Liu, Ziqing Ma, Linxiao Yang, Tian Zhou, Rui Xia, Yi Wang, Qingsong Wen, Liang Sun, "SaDI: A Self-Adaptive Decomposed Interpretable Framework for Electricity Load Forecasting under Extreme Events", in Proc. IEEE 48th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2023), Rhodes Island, Greece, June 2023. [arXiv]
[C42] [AAAI'23] Yuxuan Liang, Yutong Xia, Songyu Ke, Yiwei Wang, Qingsong Wen, Junbo Zhang, Yu Zheng, Roger Zimmermann, "AirFormer: Predicting Nationwide Air Quality in China with Transformers", in Proc. of The 37th AAAI Conference on Artificial Intelligence (AAAI 2023), Washington DC, USA, Feb. 2023. [arXiv] [code]
[C41] [AAAI'23] Zhiqiang Zhou, Chaoli Zhang, Lingna Ma, Jing Gu, Huajie Qian, Qingsong Wen, Liang Sun, Peng Li, Zhimin Tang, "AHPA: Adaptive Horizontal Pod Autoscaling Systems on Alibaba Cloud Container Service for Kubernetes", in Proc. AAAI Conference on Artificial Intelligence and 35th Annual Conference on Innovative Applications of Artificial Intelligence (AAAI/IAAI 2023), Washington DC, USA, Feb. 2023. [arXiv] (AAAI/IAAI 2023 Deployed Application Award)
[C40] [AAAI'23] Zhaoyang Zhu, Weiqi Chen, Rui Xia, Tian Zhou, Peisong Niu, Bingqing Peng, Wenwei Wang, Hengbo Liu, Ziqing Ma, Qingsong Wen, Liang Sun, "eForecaster: Unifying Electricity Forecasting with Robust, Flexible, and Explainable Machine Learning Algorithms", in Proc. AAAI Conference on Artificial Intelligence and 35th Annual Conference on Innovative Applications of Artificial Intelligence (AAAI/IAAI 2023), Washington DC, USA, Feb. 2023. [paper] (AAAI/IAAI 2023 Deployed Application Award)
[C39] [NeurIPS'22] Tian Zhou, Ziqing Ma, Xue Wang, Qingsong Wen, Liang Sun, Tao Yao, Wotao Yin, Rong Jin, "FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting", in Proc. 36th Conference on Neural Information Processing Systems (NeurIPS 2022), New Orleans, USA, Dec. 2022. [arXiv] [code] (NeurIPS Oral, Top 2%)
[C38] [NeurIPS'22] Chenxiao Yang, Qitian Wu, Qingsong Wen, Zhiqiang Zhou, Liang Sun, Junchi Yan, "Towards Out-of-Distribution Sequential Event Prediction: A Causal Treatment," in Proc. 36th Annual Conference on Neural Information Processing Systems (NeurIPS 2022), New Orleans, USA, Dec. 2022. [arXiv] [code]
[C37] [NeurIPS'22 WS] Kexin Zhang, Qingsong Wen, Chaoli Zhang, Liang Sun, Yong Liu, "Time Series Anomaly Detection using Skip-Step Contrastive Predictive Coding," in NeurIPS 2022 Workshop on Self-Supervised Learning - Theory and Practice, New Orleans, USA, Dec. 2022. [paper]
[C36] [NeurIPS'22 WS] Peisong_Niu, Tian Zhou, Qingsong Wen, Liang Sun, Tao Yao, "Chemistry Guided Molecular Graph Transformer," in NeurIPS 2022 Workshop on AI for Science: Progress and Promises, New Orleans, USA, Dec. 2022. [paper]
[C35] [ICML'22] Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, Rong Jin, "FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting," in Proc. 39th International Conference on Machine Learning (ICML 2022), Baltimore, USA, July 17-23, 2022. (Acceptance Rate=1117/5630=19.8%) [arXiv] [Oral] [code]
[C34] [KDD'22] Weiqi Chen, Wenwei Wang, Bingqing Peng, Qingsong Wen, Tian Zhou, Liang Sun, "Learning to Rotate: Quaternion Transformer for Complicated Periodical Time Series Forecasting", in Proc. 28th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD 2022), Washington DC, USA, Aug. 2022. (Research Track, Acceptance Rate 254/1695=14.99%) [paper] [Oral] [code]
[C33] [KDD'22] Qingsong Wen, Linxiao Yang, Tian Zhou, Liang Sun, "Robust Time Series Analysis and Applications: An Industrial Perspective", in Proc. 28th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD 2022), Washington DC, USA, Aug. 2022. [paper] [Website]
[C32] [CIKM'22] Chaoli Zhang, Tian Zhou, Qingsong Wen*, Liang Sun, "TFAD: A Decomposition Time Series Anomaly Detection Architecture with Time-Frequency Analysis,” in Proc. 31st ACM International Conference on Information and Knowledge Management (CIKM 2022), Atlanta, USA, Oct. 2022. [arXiv] [paper] [Oral] [code]
[C31] [CIKM'22] Xiaomin Song, Qingsong Wen, and Liang Sun, "Robust Time Series Dissimilarity Measure for Outlier Detection and Periodicity Detection,” in Proc. 31st ACM International Conference on Information and Knowledge Management (CIKM 2022), Atlanta, USA, Oct. 2022. [arXiv]
[C30] [ICASSP'22] Chaoli Zhang, Zhiqiang Zhou, Yingying Zhang, Linxiao Yang, Kai He, Qingsong Wen, Liang Sun (All authors equally contributed), "NetRCA: An Effective Network Fault Cause Localization Algorithm," in Proc. IEEE 47th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2022), Singapore, May 2022. [arXiv] [Oral] [link] (ICASSP‘22 AIOps Challenge, First Place (1/382))
[C29] [ICDE'22] Huajie Qian, Qingsong Wen, Liang Sun, Jing Gu, Qiulin Niu, Zhimin Tang, "RobustScaler: QoS-Aware Autoscaling for Complex Workloads," in Proc. IEEE 38th International Conference on Data Engineering (ICDE 2022), Kuala Lumpur, Malaysia, May 2022. [arXiv] [Oral], Media Coverage: [Mo4Tech] [Alicloudnative] [Zhihu] [1024sou]
[C28] [IJCAI'21] Qingsong Wen, Liang Sun, Fan Yang, Xiaomin Song, Jingkun Gao, Xue Wang, Huan Xu, "Time Series Data Augmentation for Deep Learning: A Survey," in the 30th International Joint Conference on Artificial Intelligence (IJCAI 2021), Montreal, Canada, Aug. 2021. [paper] [arXiv] [Oral]. Selected by Paper Digest into Most Influential IJCAI Papers (Version: 2022-02), Rank 1st (1/600+ IJCAI’21 papers).
[C27] [SIGMOD'21] Qingsong Wen, Kai He, Liang Sun, Yingying Zhang, Min Ke, Huan Xu, "RobustPeriod: Time-Frequency Mining for Robust Multiple Periodicity Detection," in Proc. ACM SIGMOD International Conference on Management of Data (SIGMOD 2021), Xi'an, China, Jun. 2021. (Research Track) [paper] [arXiv] [Oral]
[C26] [CIKM'21] Yingying Zhang, Zhengxiong Guan, Huajie Qian, Leili Xu, Hengbo Liu, Qingsong Wen, Liang Sun, Junwei Jiang, Lunting Fan, Min Ke, "CloudRCA: A Root Cause Analysis Framework for Cloud Computing Platforms," in Proc. 30th ACM International Conference on Information and Knowledge Management (CIKM 2021), Queensland, Australia, Nov. 2021. (Applied Research Track, Oral, Top 20/290=6.9%) [paper] [arXiv] [Oral]
[C25] [ICASSP'21] Qingyang Xu, Qingsong Wen, Liang Sun, "Two-Stage Framework for Seasonal Time Series Forecasting," in Proc. IEEE 46th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2021), Toronto, Canada, Jun. 2021. [arXiv] [Oral]
[C24] [ICASSP'21] Linxiao Yang, Qingsong Wen, Bo Yang, Liang Sun, "A Robust and Efficient Multi-Scale Seasonal-Trend Decomposition," in Proc. IEEE 46th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2021), Toronto, Canada, Jun. 2021. [paper] [Oral]
[C23] [KDD'20 WS] Jingkun Gao, Xiaomin Song, Qingsong Wen, Pichao Wang, Liang Sun, Huan Xu, "RobustTAD: Robust Time Series Anomaly Detection via Decomposition and Convolutional Neural Networks," in ACM SIGKDD Workshop on Mining and Learning from Time Series (KDD-MiLeTS 2020), San Diego, USA, Aug. 2020. [paper] [arXiv_extended_version]
[C22] [KDD'20] Qingsong Wen, Zhe Zhang, Yan Li, Liang Sun, "Fast RobustSTL: Efficient and Robust Seasonal-Trend Decomposition for Time Series with Complex Patterns," in Proc. 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD 2020), San Diego, USA, Aug. 2020. (Research Track, Acceptance Rate 216/1279=16.9%) [paper] [Oral]
[C21] [ICASSP'20] Qingsong Wen, Zhengzhi Ma, Liang Sun, "On Robust Variance Filtering and Change of Variance Detection," in Proc. IEEE 45th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2020), Barcelona, Spain, May 2020. [paper] [Oral]
[C20] [IJCAI'19] Qingsong Wen, Jingkun Gao, Xiaomin Song, Liang Sun, Jian Tan, "RobustTrend: A Huber Loss with a Combined First and Second Order Difference Regularization for Time Series Trend Filtering," in Proc. 28th International Joint Conference on Artificial Intelligence (IJCAI 2019), Macao, China, Aug. 2019. (Acceptance Rate 850/4752=17.9%) [arXiv] [paper] [Oral]
[C19] [AAAI'19] Qingsong Wen, Jingkun Gao, Xiaomin Song, Liang Sun, Huan Xu, Shenghuo Zhu, "RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series," in Proc. 33th AAAI Conference on Artificial Intelligence (AAAI 2019), Honolulu, Hawaii, USA, Jan. 2019. (Acceptance Rate 1150/7095=16.2%) [arXiv] [paper] [Oral] [3rd-party Code], Media Coverage: [Alibaba Tech]
[C18] [SIGMOD'17 WS] Qingsong Wen, Ren Chen, Lifeng Nai, Li Zhou, Yinglong Xia, "Finding Top K Shortest Simple Paths with Improved Space Efficiency," in ACM SIGMOD Workshop on Graph Data-management Experiences and Systems (SIGMOD-GRADES 2017), Chicago, USA, May 2017. [paper]
Signal Processing, Wireless Communications, and VLSI/FPGA: LLL, Lattice Reduction, MIMO, Peak to Average Power Ratio (PAPR) Reduction, OFDM, B3G/4G/5G, etc.
[C17] [ICASSP'16] Qingsong Wen and Xiaoli Ma, “Fixed-complexity variants of the effective LLL algorithm with greedy convergence for MIMO detection,” in Proc. IEEE 41th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2016), Shanghai, China, Mar. 2016, pp. 3826-3830.
[C16] [ISCAS'16] Qingsong Wen and Xiaoli Ma, “VLSI implementation of incremental fixed-complexity LLL lattice reduction for MIMO detection,” in Proc. IEEE International Symposium on Circuits and Systems (ISCAS 2016), Montreal, Canada, May 2016, pp. 1898-1901.
[C15] [GLOBECOM'14] Qingsong Wen, Qi Zhou, and Xiaoli Ma, “An enhanced fixed-complexity LLL algorithm for MIMO detection,” in Proc. IEEE Global Communications Conference (GLOBECOM 2014), Austin, TX, Dec. 2014, pp. 3231-3236.
[C14] [MILCOM'14] Qingsong Wen and Xiaoli Ma, “An efficient greedy LLL algorithm for MIMO detection,” in Proc. IEEE 33th Military Communications Conference (MILCOM 2014), Baltimore, MD, Oct. 2014, pp. 550-555.
[C13] [ICASSP'13] Qingsong Wen, Qi Zhou, Chunming Zhao, and Xiaoli Ma, “Fixed-point realization of lattice-reduction aided MIMO receivers with complex K-best algorithm,” in Proc. IEEE 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013), Vancouver, Canada, May 2013, pp. 5031-5035.
[C12] [ASILOMAR'12] Qingsong Wen, Sungeun Lee, and Xiaoli Ma, "Clipping effect on radiation pattern in downtilt beamforming," in Proc. IEEE 46th Asilomar Conference on Signals, Systems, and Computers (ASILOMAR 2012), Pacific Grove, CA, Nov. 4-7, 2012, pp. 1873-1877.
[C11] [ASILOMAR'12] Sungeun Lee, Xiaoli Ma, and Qingsong Wen, "Transmitter-side timing adjustment to mitigate interference between multiple nodes for OFDMA mesh network," in Proc. IEEE 46th Asilomar Conference on Signals, Systems, and Computers (ASILOMAR 2012), Pacific Grove, CA, Nov. 4-7, 2012, pp. 1957-1961.
[C10] [NSWCTC'09] Qihui Liang, Qingsong Wen, Yue Xiao, Shaoqian Li, “A comparison of SCR and active-Set methods for PAPR reduction in OFDM systems," in Proc. International Conference on Networks Security, Wireless Communications and Trusted Computing (NSWCTC 2009), Wuhan, China, Apr. 25-26, 2009, pp. 489-495.
[C9] [VTC'08] Qingsong Wen, Yue Xiao, Peng Cheng, Lilin Dan, Shaoqian Li, “A modified partial transmit sequence scheme for PAPR reduction in OFDM system,” in Proc. IEEE 68th Vehicular Technology Conference (VTC 2008), Calgary, Canada, Sept.21-24, 2008, pp. 1-5.
[C8] [WiCOM'08] Qingsong Wen, Yue Xiao, Peng Cheng, Cong Zhang, Shaoqian Li, "S-PTS for PAPR reduction in OFDM systems," in Proc. IEEE 4th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM 2008), Dalian, China, Oct. 12-14, 2008, pp. 1-4.
[C7] [ICCS'08] Peng Cheng, Qingsong Wen, Haichao Guo, Yue Xiao, Shaoqian Li, "Maximum diversity gain for single-antenna vector OFDM system," in Proc. IEEE 11th International Conference on Communication Systems (ICCS 2008), Guangzhou, China, Nov 19-21, 2008, pp. 1050-1054.
[C6] [AICT'08] Xu He, Qingsong Wen, Yue Xiao, Shaoqian Li, "A novel method for reducing the side information of MSR-OFDM system," in Proc. Fourth Advanced International Conference on Telecommunications (AICT 2008), Athens, Greece, Jun. 8-13, 2008, pp. 404-407.
[C5] [ICCCAS'07] Qingsong Wen, Yue Xiao, Shaoqian Li, "LC-APTS for PAPR reduction in OFDM systems," in Proc. IEEE International Conference on Communications, Circuits and Systems (ICCCAS 2007), Kokura, Japan, Jul, 11-13, 2007, pp. 280-283.
[C4] [AICT'07] Yue Xiao, Qingsong Wen, Xia Lei, Shaoqian Li, "Improved PTS for PAPR reduction in OFDM systems," in Proc. Fourth Advanced International Conference on Telecommunications (AICT 2007), Morne, Mauritius, May 13-19, 2007, pp. 1-4.
[C3] [VTC'07] Lilin Dan, Qingsong Wen, Yue Xiao and Shaoqian Li, "A novel user grouping scheme for PAPR reduction in MC-CDMA system," in Proc. IEEE 66th Vehicular Technology Conference (VTC 2007), Baltimore, MD, Sep. 30 - Oct. 3, 2007, pp 994-998.
[C2] [MCWC'06] Sisi Liu, Yue Xiao, Qingsong Wen and Shaoqian Li, "A low complexity code selection method for peak to average power ratio reduction in MC-CDMA," in Proc. International Conference on Mobile Computing and Wireless Communication (MCWC 2006), Amman, Jordan, Sep.17-20, 2006, pp. 139-144.
[C1] [ICCCAS'06] Qingsong Wen, Yue Xiao, Shaoqian Li, H. Kayama, Chunlin Yan, "The implement of low-PAPR OFDM system," in Proc. IEEE International Conference on Communications, Circuits and Systems (ICCCAS 2006), Guilin, China, Jun. 25-28, 2006, pp. 1226-1229.
[Journal Papers]
( PIEEE x1, TPAMI x1, TKDE x1, TSG x5, TPWRS x1, TWC x1, etc. )
[J19] [TPAMI'24] Kexin Zhang, Qingsong Wen*, Chaoli Zhang, Rongyao Cai, Ming Jin, Yong Liu, James Zhang, Yuxuan Liang, Guansong Pang, Dongjin Song, Shirui Pan, "Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects," IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2024. (CCF-A, IF=23.6) [arXiv] [paper] [Website]
[J18] [IS'24] Tianlong Xu, Richard Tong, Jing Liang, Xing Fan, Xing Fan, Qingsong Wen, "Foundation Models for Education: Promises and Prospects", IEEE Intelligent Systems, 2024. [arXiv] [paper]
[J17] [TSG'24] Yangze Zhou, Qingsong Wen, Jie Song, Xueyuan Cui, and Yi Wang, “Load Data Valuation in Multi-Energy Systems: An End-to-End Approach,” IEEE Transactions on Smart Grid (TSG), 2024. [paper] [arXiv]
[J16] [TSG'24] Nan Lu, Shu Liu, Qingsong Wen, Qiming Chen, Liang Sun, Yi Wang, “Federated Domain Separation for Distributed Forecasting of Non-IID Household Loads,” IEEE Transactions on Smart Grid (TSG), 2024. [paper]
[J15] [TSG'23] Dalin Qin, Chenxi Wang, Qingsong Wen, Weiqi Chen, Liang Sun, and Yi Wang, “Personalized Federated DARTS for Electricity Load Forecasting of Individual Buildings,” IEEE Transactions on Smart Grid (TSG), 2023. (IF=10.275) [paper]
[J14] [TSG'23] Chenxi Wang, Yangze Zhou, Qingsong Wen, and Yi Wang, “Improving Load Forecasting Performance via Sample Reweighting,” IEEE Transactions on Smart Grid (TSG), 2023. (IF=10.275) [paper]
[J13] [TSG'23] Miha Grabner, Yi Wang, Qingsong Wen, Boštjan Blažič, Vitomir Štruc, “A Global Modeling Approach for Load Forecasting in Distribution Networks”, IEEE Transactions on Smart Grid (TSG), 2023. (IF=10.275) [arXiv] [paper] [code]
[J12] [AI Magazine'23] Zhaoyang Zhu, Weiqi Chen, Rui Xia, Tian Zhou, Peisong Niu, Bingqing Peng, Wenwei Wang, Hengbo Liu, Ziqing Ma, Xinyue Gu, Jin Wang, Qiming Chen, Linxiao Yang, Qingsong Wen, Liang Sun, “Energy Forecasting with Robust, Flexible, and Explainable Machine Learning Algorithms,” AAAI AI Magazine, 2023. [paper]
[J11] [CEP'23] Qiming Chen, Qingsong Wen, Xialai Wu, Xun Lang, Yao Shi, Lei Xie, Hongye Su, “Detection and time–frequency analysis of multiple plant-wide oscillations using adaptive multivariate intrinsic chirp component decomposition,” IFAC Control Engineering Practice, 2023. (IF=4.9) [paper]
[J10] [AAS'23] Qiming Chen, Qingsong Wen, Xun Lang, Lei Xie, Hongye Su, "Univariate and Multivariate Signal Decomposition: Review and Future Directions", in Acta Automatica Sinica (AAS), 2023. (CCF-A) [paper]
[J9] [RPG'23] Jiahao Ma, Ning Zhang, Qingsong Wen, Yi Wang, "An Efficient Local Multi-Energy Systems Planning Method with Long-term Storage," IET Renewable Power Generation, 2023. (IF=3.034) [paper]
[J8] [APEN'23] Yi Wang, Jiahao Ma, Ning Gao, Qingsong Wen, Liang Sun, and Hongye Guo, “Federated Fuzzy K-means for Privacy-Preserving Behavior Analysis in Smart Grids,” Applied Energy (APEN), 2023. (IF=11.446) [paper]
[J7] [PIEEE'23] Yi Wang, Chien-fei Chen, Peng-Yong Kong, Husheng Li, and Qingsong Wen, “A Cyber-Physical-Social Perspective on Future Smart Distribution Systems,” Proceedings of the IEEE (PIEEE), 2023. (IF=20.6) [paper]
[J6] [TKDE'22] Longyuan Li, Junchi Yan, Qingsong Wen, Yaohui Jin, and Xiaokang Yang, "Learning Robust Deep State Space for Unsupervised Anomaly Detection in Contaminated Time-Series," IEEE Transactions on Knowledge and Data Engineering (TKDE), 2022. (IF=9.235) [paper]
[J5] [iEnergy'22] Chenxi Wang, Dalin Qin, Qingsong Wen, Tian Zhou, Liang Sun, and Yi Wang, “Adaptive Probabilistic Load Forecasting for Individual Buildings,” iEnergy, 2022. [paper]
[J4] [TPWRS'22] Yihong Zhou, Zhaohao Ding, Qingsong Wen, Yi Wang, "Robust Load Forecasting towards Adversarial Attacks via Bayesian Learning," IEEE Transactions on Power Systems (TPWRS), 2022. (IF=7.326) [paper]
[J3] [TWC'16] Qingsong Wen and Xiaoli Ma, “Efficient Greedy LLL Algorithms for Lattice Decoding,” IEEE Transactions on Wireless Communications (TWC 2016), vol. 15, no. 5, pp. 3560-3572, May 2016. (IF=8.346) [paper]
[J2] [WPC'10] Su Hu, Gang Wu, Qingsong Wen, Yue Xiao, Shaoqian Li, “Nonlinearity Reduction by Tone Reservation with Null Subcarriers for WiMAX System,” Springer Wireless Personal Communications (WPC), vol. 54, no. 2, pp. 289-305, 2010. [paper]
[J1] [SPL'07] Yue Xiao, Xia Lei, Qingsong Wen, Shaoqian Li, “A Class of Low Complexity PTS Techniques for PAPR Reduction in OFDM Systems,” IEEE Signal Processing Letters. (SPL), vol. 14, no. 10, Oct. 2007. [paper]
[arXiv Papers]
LLM, LVLM, Agent, GNN, etc.:
Beyond LLaVA-HD: Diving into High-Resolution Large Multimodal Models, [arXiv] [code]
AutoSurvey: Large Language Models Can Automatically Write Surveys, [arXiv]
CulturePark: Boosting Cross-cultural Understanding in Large Language Models, [arXiv]
Debiasing Large Visual Language Models, [arXiv]
WeaverBird: Empowering Financial Decision-Making with Large Language Model, Knowledge Base, and Search Engine, [arXiv] [code]
Intelligent Cross-Organizational Process Mining: A Survey and New Perspectives, [arXiv]
Generative Semi-supervised Graph Anomaly Detection, [arXiv]
AI/GenAI/LLM for Education:
Knowledge Tagging System on Math Questions via LLMs with Flexible Demonstration Retriever, [arXiv]
Large Language Models for Education: A Survey and Outlook, [arXiv]
Automate Knowledge Concept Tagging on Math Questions with LLMs, [arXiv]
Bringing Generative AI to Adaptive Learning in Education, [arXiv]
Developing and Deploying Industry Standards for Artificial Intelligence in Education (AIED): Challenges, Strategies, and Future Directions, [arXiv]
AI/GenAI/LLM for Time Series & Spatio-Temporal Data:
TSI-Bench: Benchmarking Time Series Imputation, [arXiv] [Website]
A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges, [arXiv]
Time-MMD: A New Multi-Domain Multimodal Dataset for Time Series Analysis, [arXiv] [Website]
TwinS: Revisiting Non-Stationarity in Multivariate Time Series Forecasting, [arXiv]
Time-SSM: Simplifying and Unifying State Space Models for Time Series Forecasting, [arXiv]
A Survey on Diffusion Models for Time Series and Spatio-Temporal Data, [arXiv] [Website]
Time-FFM: Towards LM-Empowered Federated Foundation Model for Time Series Forecasting, [arXiv]
Addressing Concept Shift in Online Time Series Forecasting: Detect-then-Adapt, [arXiv]
Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook, [arXiv] [Website]
UrbanVLP: A Multi-Granularity Vision-Language Pre-Trained Foundation Model for Urban Indicator Prediction, [arXiv]
Deep Learning for Trajectory Data Management and Mining: A Survey and Beyond, [arXiv] [Website]
PDETime: Rethinking Long-Term Multivariate Time Series Forecasting from the perspective of partial differential equations, [arXiv]
Attractor Memory for Long-Term Time Series Forecasting: A Chaos Perspective, [arXiv]
Deep Learning for Multivariate Time Series Imputation: A Survey, [arXiv] [Website]
A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection, [arXiv] [Website]
Benchmarks and Custom Package for Electrical Load Forecasting, [arXiv] [Website]
DiffLoad: Uncertainty Quantification in Load Forecasting with Diffusion Model, [arXiv] [code]
How Expressive are Spectral-Temporal Graph Neural Networks for Time Series Forecasting, [arXiv]
TreeDRNet: A Robust Deep Model for Long Term Time Series Forecasting, [arXiv]
[Book Chapter]
[B1] Qingsong Wen, Ren Chen, Yinglong Xia, Li Zhou, Juan Deng, Jian Xu, Mingzhen Xia, "Big-Data Helps SDN to Verify Integrity of Control/Data Planes," in Big-Data and Software Defined Networking, IET Book Series on Big Data, ISBN: 9781785613043, DOI: 10.1049/PBPC015E Mar. 2018. [Invited Book Chapter] [link] [pdf]
[Patents]
[P16] "A Hotspot Partition Positioning Scheme based on Comprehensive Index Sorting in NoSQL System", in Patent Application, filed in 2023.
[P15] "A Time Series Forecasting System with Robustness Against Anomalies", in Patent Application, filed in 2023.
[P14] "An Efficient Method for Anomaly Detection of Time Series Data Based on Contrastive Representation Learning", in Patent Application, filed in 2023.
[P13] "A General Robust Multi-Period Detection System for Time Series Data", in Patent Application, filed in 2023.
[P12] "A Time Series Subsequence Anomaly Detection System Based on Graph Neural Network", in Patent Application, filed in 2023.
[P11] "A Cloud Resource Automatic Scaling Algorithm Based on Probabilistic Load Forecasting", in Patent Application, filed in 2023.
[P10] "A Universal Forecasting System for Heterogeneous Power Time Series Based on Automatic Machine Learning", in Patent Application, filed in 2023.
[P9] "An Effective System for Locating Causes of Network Faults", in Patent Application, filed in 2022.
[P8] Xiaomin Song, Jingkun Gao, Qingsong Wen, Bo Yang, Kai He, Yan Li, Juan Bian, Liang Sun. "Robust Time Series Anomaly Detection System for Commercial Data," in Patent Application, filed in Jan 2020.
[P7] Xiaomin Song, Jingkun Gao, Qingsong Wen, Bo Yang, Kai He, Yan Li, Juan Bian, Liang Sun. "Classification Enhanced Time Series Anomaly Detection System," in Patent Application, filed in Jan 2020.
[P6] Xiaomin Song, Qingsong Wen, Yan Li, Liang Sun, "Distance Measurement for Time Series," in US Patent Pending, No.: US20220156321A1, 2022 [link]
[P5] Qingsong Wen, Liang Sun, and Huan Xu, "Change of Variance Detection in Time Series Data," in US Patent No.: US 11222093, Jan 11, 2022; in China Patent No. CN113496080A, Oct. 12, 2021. [link]
[P4] Qingsong Wen, Jingkun Gao, Xiaomin Song, Liang Sun, Huan Xu, Wotao Yin, Tao Yao, "Time Series Decomposition," in US Patent No.: US 11146445, Oct 12, 2021; in China Patent No. CN112989271A, Jun. 18, 2021. [link]
[P3] Qingsong Wen, Kai He, Jingkun Gao, Liang Sun, Jian Tan, Tao Yaoo, "Periodicity Detection and Period Length Estimation in Time Series," in US Patent No.: US 11132342, Sep 28, 2021; in China Patent No. CN112989266A, Jun. 18, 2021. [link]
[P2] Xiaoli Ma and Qingsong Wen, "Multi-Input Multi-Output (MIMO) Detection Systems," in US Patent No.: US 9,705,646 B2, Jul. 11, 2017. [link]
[P1] Lilin Dan, Yue Xiao, Yongrui Peng, Ten Li, Qingsong Wen, Shaoqian Li, Chunlin Yan, Zhan Zhang, and Hidetoshi Kayama, "Method and Device for Processing Multi-carrier Signal," in China Patent No. CN101729475, Japan Patent No. JP2010098734, 2013. [link]