2025
OneForecast: A Universal Framework for Global and Regional Weather Forecasting.
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CoRR, February, 2025
Deciding When to Use a Personalized Model for Load Forecasting.
IEEE Trans. Smart Grid, January, 2025
Noise-Resilient Point-wise Anomaly Detection in Time Series Using Weak Segment Labels.
CoRR, January, 2025
CultureVLM: Characterizing and Improving Cultural Understanding of Vision-Language Models for over 100 Countries.
CoRR, January, 2025
LLM-Virus: Evolutionary Jailbreak Attack on Large Language Models.
CoRR, January, 2025
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.
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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
Ask-Before-Detection: Identifying and Mitigating Conformity Bias in LLM-Powered Error Detector for Math Word Problem Solutions.
CoRR, 2024
A Survey of Mathematical Reasoning in the Era of Multimodal Large Language Model: Benchmark, Method & Challenges.
CoRR, 2024
GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network.
CoRR, 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.
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CoRR, 2024
Toward Physics-guided Time Series Embedding.
CoRR, 2024
ErrorRadar: Benchmarking Complex Mathematical Reasoning of Multimodal Large Language Models Via Error Detection.
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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?
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CoRR, 2024
RAGLAB: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented Generation.
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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.
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CoRR, 2024
A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges.
CoRR, 2024
Time-MMD: A New Multi-Domain Multimodal Dataset for Time Series Analysis.
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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
A Survey on Diffusion Models for Time Series and Spatio-Temporal Data.
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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.
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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
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
Task-oriented Time Series Imputation Evaluation via Generalized Representers.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024
AutoSurvey: Large Language Models Can Automatically Write Surveys.
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Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024
Generative Semi-supervised Graph Anomaly Detection.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024
Time-MMD: Multi-Domain Multimodal Dataset for Time Series Analysis.
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Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024
Time-FFM: Towards LM-Empowered Federated Foundation Model for Time Series Forecasting.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024
CulturePark: Boosting Cross-cultural Understanding in Large Language Models.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024
Attractor Memory for Long-Term Time Series Forecasting: A Chaos Perspective.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 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.
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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.
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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.
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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.
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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.
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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.
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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.
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CoRR, 2023
Benchmarks and Custom Package for Electrical Load Forecasting.
CoRR, 2023
Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects.
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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.
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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