Qingsong Wen

Orcid: 0000-0003-4516-2524

According to our database1, Qingsong Wen authored at least 145 papers between 2007 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
LogoRA: Local-Global Representation Alignment for Robust Time Series Classification.
IEEE Trans. Knowl. Data Eng., December, 2024

A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection.
IEEE Trans. Pattern Anal. Mach. Intell., December, 2024

Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects.
IEEE Trans. Pattern Anal. Mach. Intell., October, 2024

Load Data Valuation in Multi-Energy Systems: An End-to-End Approach.
IEEE Trans. Smart Grid, September, 2024

Federated Domain Separation for Distributed Forecasting of Non-IID Household Loads.
IEEE Trans. Smart Grid, July, 2024

Rethinking self-supervised learning for time series forecasting: A temporal perspective.
Knowl. Based Syst., 2024

Foundation Models for Education: Promises and Prospects.
IEEE Intell. Syst., 2024

NetSafe: Exploring the Topological Safety of Multi-agent Networks.
CoRR, 2024

Abnormality Forecasting: Time Series Anomaly Prediction via Future Context Modeling.
CoRR, 2024

FoundTS: Comprehensive and Unified Benchmarking of Foundation Models for Time Series Forecasting.
CoRR, 2024

Task-oriented Time Series Imputation Evaluation via Generalized Representers.
CoRR, 2024

Toward Physics-guided Time Series Embedding.
CoRR, 2024

ErrorRadar: Benchmarking Complex Mathematical Reasoning of Multimodal Large Language Models Via Error Detection.
CoRR, 2024

Mind Scramble: Unveiling Large Language Model Psychology Via Typoglycemia.
CoRR, 2024

Evolving Multi-Scale Normalization for Time Series Forecasting under Distribution Shifts.
CoRR, 2024

Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts.
CoRR, 2024

AI-Driven Virtual Teacher for Enhanced Educational Efficiency: Leveraging Large Pretrain Models for Autonomous Error Analysis and Correction.
CoRR, 2024

Knowledge Tagging with Large Language Model based Multi-Agent System.
CoRR, 2024

Time Series Analysis for Education: Methods, Applications, and Future Directions.
CoRR, 2024

MME-RealWorld: Could Your Multimodal LLM Challenge High-Resolution Real-World Scenarios that are Difficult for Humans?
CoRR, 2024

RAGLAB: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented Generation.
CoRR, 2024

Unlocking the Power of LSTM for Long Term Time Series Forecasting.
CoRR, 2024

Intelligent Cross-Organizational Process Mining: A Survey and New Perspectives.
CoRR, 2024

Knowledge Tagging System on Math Questions via LLMs with Flexible Demonstration Retriever.
CoRR, 2024

TSI-Bench: Benchmarking Time Series Imputation.
CoRR, 2024

A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges.
CoRR, 2024

AutoSurvey: Large Language Models Can Automatically Write Surveys.
CoRR, 2024

Time-MMD: A New Multi-Domain Multimodal Dataset for Time Series Analysis.
CoRR, 2024

Beyond LLaVA-HD: Diving into High-Resolution Large Multimodal Models.
CoRR, 2024

TwinS: Revisiting Non-Stationarity in Multivariate Time Series Forecasting.
CoRR, 2024

Predicting Parking Availability in Singapore with Cross-Domain Data: A New Dataset and A Data-Driven Approach.
CoRR, 2024

Time-SSM: Simplifying and Unifying State Space Models for Time Series Forecasting.
CoRR, 2024

CulturePark: Boosting Cross-cultural Understanding in Large Language Models.
CoRR, 2024

Time-FFM: Towards LM-Empowered Federated Foundation Model for Time Series Forecasting.
CoRR, 2024

A Survey on Diffusion Models for Time Series and Spatio-Temporal Data.
CoRR, 2024

Large Language Models for Education: A Survey and Outlook.
CoRR, 2024

Automate Knowledge Concept Tagging on Math Questions with LLMs.
CoRR, 2024

UrbanVLP: A Multi-Granularity Vision-Language Pre-Trained Foundation Model for Urban Indicator Prediction.
CoRR, 2024

Addressing Concept Shift in Online Time Series Forecasting: Detect-then-Adapt.
CoRR, 2024

Developing and Deploying Industry Standards for Artificial Intelligence in Education (AIED): Challenges, Strategies, and Future Directions.
CoRR, 2024

Deep Learning for Trajectory Data Management and Mining: A Survey and Beyond.
CoRR, 2024

Debiasing Multimodal Large Language Models.
CoRR, 2024

PDETime: Rethinking Long-Term Multivariate Time Series Forecasting from the perspective of partial differential equations.
CoRR, 2024

Bringing Generative AI to Adaptive Learning in Education.
CoRR, 2024

Generative Semi-supervised Graph Anomaly Detection.
CoRR, 2024

Attractor Memory for Long-Term Time Series Forecasting: A Chaos Perspective.
CoRR, 2024

Deep Learning for Multivariate Time Series Imputation: A Survey.
CoRR, 2024

Position Paper: What Can Large Language Models Tell Us about Time Series Analysis.
CoRR, 2024

HiMTM: Hierarchical Multi-Scale Masked Time Series Modeling for Long-Term Forecasting.
CoRR, 2024

UrbanCLIP: Learning Text-enhanced Urban Region Profiling with Contrastive Language-Image Pretraining from the Web.
Proceedings of the ACM on Web Conference 2024, 2024

LARA: A Light and Anti-overfitting Retraining Approach for Unsupervised Time Series Anomaly Detection.
Proceedings of the ACM on Web Conference 2024, 2024

Stable Synthetic Control with Anomaly Detection for Causal Inference.
Proceedings of the 2024 SIAM International Conference on Data Mining, 2024

LogParser-LLM: Advancing Efficient Log Parsing with Large Language Models.
Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

AI for Education (AI4EDU): Advancing Personalized Education with LLM and Adaptive Learning.
Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

The 10th Mining and Learning from Time Series Workshop: From Classical Methods to LLMs.
Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

Foundation Models for Time Series Analysis: A Tutorial and Survey.
Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

Heterogeneity-Informed Meta-Parameter Learning for Spatiotemporal Time Series Forecasting.
Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

Cluster-Wide Task Slowdown Detection in Cloud System.
Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

Predicting Carpark Availability in Singapore with Cross-Domain Data: A New Dataset and A Data-Driven Approach.
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 2024

BayOTIDE: Bayesian Online Multivariate Time Series Imputation with Functional Decomposition.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Position: What Can Large Language Models Tell Us about Time Series Analysis.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Online GNN Evaluation Under Test-time Graph Distribution Shifts.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

EasyTPP: Towards Open Benchmarking Temporal Point Processes.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

CARD: Channel Aligned Robust Blend Transformer for Time Series Forecasting.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Explaining Time Series via Contrastive and Locally Sparse Perturbations.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

RobustTSF: Towards Theory and Design of Robust Time Series Forecasting with Anomalies.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Pathformer: Multi-scale Transformers with Adaptive Pathways for Time Series Forecasting.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Time-LLM: Time Series Forecasting by Reprogramming Large Language Models.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Learning Multi-Pattern Normalities in the Frequency Domain for Efficient Time Series Anomaly Detection.
Proceedings of the 40th IEEE International Conference on Data Engineering, 2024

RobustTSVar: A Robust Time Series Variance Estimation Algorithm.
Proceedings of the IEEE International Conference on Acoustics, 2024

Skip-Step Contrastive Predictive Coding for Time Series Anomaly Detection.
Proceedings of the IEEE International Conference on Acoustics, 2024

HiMTM: Hierarchical Multi-Scale Masked Time Series Modeling with Self-Distillation for Long-Term Forecasting.
Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, 2024

Advancing Multivariate Time Series Anomaly Detection: A Comprehensive Benchmark with Real-World Data from Alibaba Cloud.
Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, 2024

AI Agent for Information Retrieval: Generating and Ranking.
Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, 2024

RCAgent: Cloud Root Cause Analysis by Autonomous Agents with Tool-Augmented Large Language Models.
Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, 2024

2023
Weakly Guided Adaptation for Robust Time Series Forecasting.
Proc. VLDB Endow., December, 2023

Energy forecasting with robust, flexible, and explainable machine learning algorithms.
AI Mag., December, 2023

Personalized Federated DARTS for Electricity Load Forecasting of Individual Buildings.
IEEE Trans. Smart Grid, November, 2023

A Global Modeling Framework for Load Forecasting in Distribution Networks.
IEEE Trans. Smart Grid, November, 2023

Improving Load Forecasting Performance via Sample Reweighting.
IEEE Trans. Smart Grid, July, 2023

A Cyber-Physical-Social Perspective on Future Smart Distribution Systems.
Proc. IEEE, July, 2023

Learning Robust Deep State Space for Unsupervised Anomaly Detection in Contaminated Time-Series.
IEEE Trans. Knowl. Data Eng., June, 2023

MagicScaler: Uncertainty-aware, Predictive Autoscaling.
Proc. VLDB Endow., 2023

MACE: A Multi-pattern Accommodated and Efficient Anomaly Detection Method in the Frequency Domain.
CoRR, 2023

When Urban Region Profiling Meets Large Language Models.
CoRR, 2023

RCAgent: Cloud Root Cause Analysis by Autonomous Agents with Tool-Augmented Large Language Models.
CoRR, 2023

Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook.
CoRR, 2023

LARA: A Light and Anti-overfitting Retraining Approach for Unsupervised Anomaly Detection.
CoRR, 2023

BayOTIDE: Bayesian Online Multivariate Time series Imputation with functional decomposition.
CoRR, 2023

EasyTPP: Towards Open Benchmarking the Temporal Point Processes.
CoRR, 2023

Benchmarks and Custom Package for Electrical Load Forecasting.
CoRR, 2023

Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects.
CoRR, 2023

DiffLoad: Uncertainty Quantification in Load Forecasting with Diffusion Model.
CoRR, 2023

Make Transformer Great Again for Time Series Forecasting: Channel Aligned Robust Dual Transformer.
CoRR, 2023

How Expressive are Spectral-Temporal Graph Neural Networks for Time Series Forecasting?
CoRR, 2023

OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensembling.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

ADGym: Design Choices for Deep Anomaly Detection.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection.
Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

The 9th SIGKDD International Workshop on Mining and Learning from Time Series.
Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

Transformers in Time Series: A Survey.
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

Robust Dominant Periodicity Detection for Time Series with Missing Data.
Proceedings of the IEEE International Conference on Acoustics, 2023

SADI: A Self-Adaptive Decomposed Interpretable Framework for Electric Load Forecasting Under Extreme Events.
Proceedings of the IEEE International Conference on Acoustics, 2023

DiffSTG: Probabilistic Spatio-Temporal Graph Forecasting with Denoising Diffusion Models.
Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems, 2023

eForecaster: Unifying Electricity Forecasting with Robust, Flexible, and Explainable Machine Learning Algorithms.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

AHPA: Adaptive Horizontal Pod Autoscaling Systems on Alibaba Cloud Container Service for Kubernetes.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

AirFormer: Predicting Nationwide Air Quality in China with Transformers.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022
TreeDRNet: A Robust Deep Model for Long Term Time Series Forecasting.
CoRR, 2022

A Global Modeling Approach for Load Forecasting in Distribution Networks.
CoRR, 2022

FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Towards Out-of-Distribution Sequential Event Prediction: A Causal Treatment.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Robust Time Series Analysis and Applications: An Industrial Perspective.
Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

Learning to Rotate: Quaternion Transformer for Complicated Periodical Time Series Forecasting.
Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting.
Proceedings of the International Conference on Machine Learning, 2022

RobustScaler: QoS-Aware Autoscaling for Complex Workloads.
Proceedings of the 38th IEEE International Conference on Data Engineering, 2022

Netrca: An Effective Network Fault Cause Localization Algorithm.
Proceedings of the IEEE International Conference on Acoustics, 2022

TFAD: A Decomposition Time Series Anomaly Detection Architecture with Time-Frequency Analysis.
Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022

Robust Time Series Dissimilarity Measure for Outlier Detection and Periodicity Detection.
Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022

2021
RobustPeriod: Robust Time-Frequency Mining for Multiple Periodicity Detection.
Proceedings of the SIGMOD '21: International Conference on Management of Data, 2021

Time Series Data Augmentation for Deep Learning: A Survey.
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

A Robust and Efficient Multi-Scale Seasonal-Trend Decomposition.
Proceedings of the IEEE International Conference on Acoustics, 2021

Two-Stage Framework for Seasonal Time Series Forecasting.
Proceedings of the IEEE International Conference on Acoustics, 2021

CloudRCA: A Root Cause Analysis Framework for Cloud Computing Platforms.
Proceedings of the CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1, 2021

2020
Time Series Data Augmentation for Deep Learning: A Survey.
CoRR, 2020

RobustTAD: Robust Time Series Anomaly Detection via Decomposition and Convolutional Neural Networks.
CoRR, 2020

RobustPeriod: Time-Frequency Mining for Robust Multiple Periodicities Detection.
CoRR, 2020

Fast RobustSTL: Efficient and Robust Seasonal-Trend Decomposition for Time Series with Complex Patterns.
Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

On Robust Variance Filtering and Change of Variance Detection.
Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

2019
RobustTrend: A Huber Loss with a Combined First and Second Order Difference Regularization for Time Series Trend Filtering.
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series.
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2017
Finding Top K Shortest Simple Paths with Improved Space Efficiency.
Proceedings of the Fifth International Workshop on Graph Data-management Experiences & Systems, 2017

2016
Efficient Greedy LLL Algorithms for Lattice Decoding.
IEEE Trans. Wirel. Commun., 2016

VLSI implementation of incremental fixed-complexity LLL lattice reduction for MIMO detection.
Proceedings of the IEEE International Symposium on Circuits and Systems, 2016

Fixed-complexity variants of the effective LLL algorithm with greedy convergence for MIMO detection.
Proceedings of the 2016 IEEE International Conference on Acoustics, 2016

2014
An Efficient Greedy LLL Algorithm for MIMO Detection.
Proceedings of the 2014 IEEE Military Communications Conference, 2014

An enhanced fixed-complexity LLL algorithm for MIMO detection.
Proceedings of the IEEE Global Communications Conference, 2014

2013
Fixed-point realization of lattice-reduction aided MIMO receivers with complex K-best algorithm.
Proceedings of the IEEE International Conference on Acoustics, 2013

2012
Clipping effect on radiation pattern in downtilt beamforming.
Proceedings of the Conference Record of the Forty Sixth Asilomar Conference on Signals, 2012

Transmitter-side timing adjustment to mitigate interference between multiple nodes for OFDMA mesh network.
Proceedings of the Conference Record of the Forty Sixth Asilomar Conference on Signals, 2012

2010
Nonlinearity Reduction by Tone Reservation with Null Subcarriers for WiMAX System.
Wirel. Pers. Commun., 2010

Comparisons of SCR and Active-set Methods for PAPR Reduction in OFDM Systems.
J. Networks, 2010

2008
A Modified Partial Transmit Sequence Scheme for PAPR Reduction in OFDM System.
Proceedings of the 68th IEEE Vehicular Technology Conference, 2008

A Novel Method for Reducing the Side Information of MSR-OFDM System.
Proceedings of the Fourth Advanced International Conference on Telecommunications, 2008

2007
A Class of Low Complexity PTS Techniques for PAPR Reduction in OFDM Systems.
IEEE Signal Process. Lett., 2007

A Novel User Grouping Scheme for PAPR Reduction in MC-CDMA System.
Proceedings of the 66th IEEE Vehicular Technology Conference, 2007

Improved PTS for PAPR Reduction in OFDM Systems.
Proceedings of the Third Advanced International Conference on Telecommunications (AICT 2007), 2007


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