Developed by the Decision Intelligence Lab at East China Normal University, in collaboration with the Department of Computer Science at Aalborg University, OpenTS advances data-driven decision intelligence by transforming complex data into AI-powered decisions, bridging cutting-edge research with real-world impact.
OpenTS-Bench is a comprehensive and fair benchmark of time series analytics, mainly including foreacsting and anaomly detection.
TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods
Xiangfei Qiu, Jilin Hu, Lekui Zhou, Xingjian Wu, Junyang Du, Buang Zhang, Chenjuan Guo, Aoying Zhou, Christian S. Jensen, Zhenli Sheng, Bin Yang
International Conference on Very Large Databases (PVLDB), 2024
[Paper] | [Code]
TAB: Unified Benchmarking of Time Series Anomaly Detection Methods
Xiangfei Qiu, Zhe Li, Wanghui Qiu, Shiyan Hu, Lekui Zhou, Xingjian Wu, Zhengyu Li, Chenjuan Guo, Aoying Zhou, Zhenli Sheng, Jilin Hu, Christian S. Jensen, Bin Yang
International Conference on Very Large Databases (PVLDB), 2025
[Paper] | [Code]
TSMF-Bench: Comprehensive and Unified Benchmarking of Foundation Models for Time Series Forecasting
Zhe Li, Xiangfei Qiu, Peng Chen, Yihang Wang, Hanyin Cheng, Yang Shu, Jilin Hu, Chenjuan Guo, Aoying Zhou, Christian S. Jensen, Bin Yang
ACM Knowledge Discovery and Data Mining (KDD), 2025
[Paper] | [Code]
OpenTS-FM is a series of time series foundation models, offering strong zero-shot and few-shot abilities, across data domains and analytics tasks. This initiative addresses the Generalization challenge.
LightGTS: A Lightweight General Time Series Forecasting Model
Yihang Wang, Yuying Qiu, Peng Chen, Yang Shu, Zhongwen Rao, Lujia Pan, Bin Yang, Chenjuan Guo
International Conference on Machine Learning (ICML), 2025
[Paper] | [Code]
AimTS: Augmented Series and Image Contrastive Learning for Time Series Classification
Yuxuan Chen, Shanshan Huang, Yunyao Cheng, Peng Chen, Zhongwen Rao, Yang Shu, Bin Yang, Lujia Pan, Chenjuan Guo
International Conference on Data Engineering (ICDE), 2025
[Paper] | [Code]
OpenTS-Auto features the AutoCTS series, aiming at addressing the A (Automation) challenge in the AGREE principles.
AutoCTS: Automated Correlated Time Series Forecasting
Xinle Wu and Dalin Zhang and Chenjuan Guo and Chaoyang He and Bin Yang and Christian S. Jensen
International Conference on Very Large Databases (PVLDB), 2021.
[Paper] | [Code]
AutoCTS+: Joint Neural Architecture and Hyperparameter Search for Correlated Time Series Forecasting
Xinle Wu and Dalin Zhang and Miao Zhang and Chenjuan Guo and Bin Yang and Christian S. Jensen
ACM Special Interest Group on Management of Data (SIGMOD), 2023.
[Paper] | [Code]
AutoCTS++: zero-shot joint neural architecture and hyperparameter search for correlated time series forecasting
Xinle Wu and Xingjian Wu and Bin Yang and Lekui Zhou and Chenjuan Guo and Xiangfei Qiu and Jilin Hu and Zhenli Sheng and Christian S. Jensen
The International Journal on Very Large Data Bases (VLDBJ), 2024.
[Paper] | [Code]
Fully Automated Correlated Time Series Forecasting in Minutes
Xinle Wu and Xingjian Wu and Dalin Zhang and Miao Zhang and Chenjuan Guo and Bin Yang and Christian S. Jensen
International Conference on Very Large Databases (PVLDB), 2024.
[Paper] | [Code]
OpenTS-DL includes deep learning models for different tasks, including forecasting, probabilistic forecasting, and anomaly detection, while addressing different challenges in AGREE.
Enhancing Time Series Forecasting through Selective Representation Spaces: A Patch Perspective
Xingjian Wu, Xiangfei Qiu, Hanyin Cheng, Zhengyu Li, Jilin Hu, Chenjuan Guo, Bin Yang
Conference on Neural Information Processing Systems (NeurIPS), 2025
[Paper] | [Code]
CrossAD: Time Series Anomaly Detection with Cross-scale Associations and Cross-window Modeling
Beibu Li, Qichao Shentu, Yang Shu, Hui Zhang, Ming Li, Ning Jin, Bin Yang, Chenjuan Guo
Conference on Neural Information Processing Systems (NeurIPS), 2025
[Paper] | [Code]
K2VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting
Xingjian Wu, Xiangfei Qiu, Hongfan Gao, Jilin Hu, Chenjuan Guo, Bin Yang
International Conference on Machine Learning (ICML), 2025
[Paper] | [Code]
CATCH: Channel-Aware Multivariate Time Series Anomaly Detection via Frequency Patching
Xingjian Wu, Xiangfei Qiu, Zhengyu Li, Yihang Wang, Jilin Hu, Chenjuan Guo, Hui Xiong, Bin Yang
International Conference on Learning Representations (ICLR), 2025
[Paper] | [Code]
DUET: Dual Clustering Enhanced Multivariate Time Series Forecasting
Xiangfei Qiu, Xingjian Wu, Yan Lin, Chenjuan Guo, Jilin Hu, Bin Yang
ACM Knowledge Discovery and Data Mining (SIGKDD), 2025
[Paper] | [Code]
Pathformer: Multi-scale Transformers with Adaptive Pathways for Time Series Forecasting
Peng Chen, Yingying Zhang, Yunyao Cheng, Yang Shu, Yihang Wang, Qingsong Wen, Bin Yang, Chenjuan Guo
International Conference on Learning Representations (ICLR), 2024
[Paper] | [Code]
MM-Path: Multi-modal, Multi-granularity Path Representation Learning
Ronghui Xu, Hanyin Cheng, Chenjuan Guo, Hongfan Gao, Jilin Hu, Sean Bin Yang, Bin Yang
ACM Knowledge Discovery and Data Mining (SIGKDD), 2025
[Paper] | [Code]
Weakly Guided Adaptation for Robust Time Series Forecasting
Yunyao Cheng, Peng Chen, Chenjuan Guo, Kai Zhao, Qingsong Wen, Bin Yang, Christian S Jensen
International Conference on Very Large Databases (PVLDB), 2023
[Paper] | [Code]
Air-DualODE: Air Quality Prediction with Physics-guided Dual Neural ODEs in Open Systems
Jindong Tian, Yuxuan Liang, Ronghui Xu, Peng Chen, Chenjuan Guo, Aoying Zhou, Lujia Pan, Zhongwen Rao, Bin Yang
International Conference on Learning Representations (ICLR), 2025
[Paper] | [Code]
DBLoss: Decomposition-based Loss Function for Time Series Forecasting
Xiangfei Qiu, Xingjian Wu, Hanyin Cheng, Xvyuan Liu, Chenjuan Guo, Jilin Hu, Bin Yang
Neural Information Processing Systems Conference (NeurIPS), 2025
[Code]
Enhancing Diversity for Data-free Quantization
Kai Zhao, Zhihao Zhuang, Miao Zhang, Chenjuan Guo, Yang Shu, Bin Yang
Computer Vision and Pattern Recognition Conference (CVPR), 2025
[Paper] | [Code]
Towards Lightweight Time Series Forecasting: a Patch-wise Transformer with Weak Data Enriching
Meng Wang, Jintao Yang, Bin Yang, Hui Li, Tongxin Gong, Bo Yang, Jiangtao Cui
International Conference on Data Engineering (ICDE), 2025
[Paper] | [Code]
Triformer: Triangular, Variable-Specific Attentions for Long Sequence Multivariate Time Series Forecasting
Razvan-Gabriel Cirstea, Chenjuan Guo, Bin Yang, Tung Kieu, Xuanyi Dong, Shirui Pan
International Joint Conference on Artificial Intelligence (IJCAI), 2022
[Paper] | [Code]
Multiple Time Series Forecasting with Dynamic Graph Modeling
Kai Zhao, Chenjuan Guo, Yunyao Cheng, Peng Han, Miao Zhang, Bin Yang
International Conference on Very Large Databases (PVLDB), 2024
[Paper] | [Code]
For more details, please refer to the individual project pages or publications.