OpenTS-FM: Time Series Foundation Models

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 (the 'G' in the AGREE principles).

Time Series Foundation Models for Anomaly Detection

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DADA: Towards A General Time Series Anomaly Detector with Adaptive Bottlenecks And Dual Adversarial Decoders

Qichao Shentu, Beibu Li, Kai Zhao, Yang Shu, Zhongwen Rao, Lujia Pan, Bin Yang, Chenjuan Guo

International Conference on Learning Representations (ICLR), 2025.

Time Series Foundation Models for Forecasting

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Aurora: Towards universal generative multimodal time series forecasting

Xingjian Wu, Jianxin Jin, Wanghui Qiu, Peng Chen, Yang Shu, Bin Yang, Chenjuan Guo

International Conference on Learning Representations (ICLR), 2026.

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CoRA: Boosting Time Series Foundation Models for Multivariate Forecasting through Correlation-aware Adapter

Hanyin Cheng, Xingjian Wu, Yang Shu, Zhongwen Rao, Lujia Pan, Bin Yang, Chenjuan Guo

International Conference on Learning Representations (ICLR), 2026.

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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.

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ROSE: Towards a General Time Series Forecasting Model with Unified Representation and Adaptive Transfer

Yihang Wang, Yuying Qiu, Peng Chen, Kai Zhao, Yang Shu, Zhongwen Rao, Lujia Pan, Bin Yang, Chenjuan Guo

International Conference on Machine Learning (ICML), 2025.

Time Series Foundation Models for Classification

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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.