Time-Series Foundation Models

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Time-series foundation models learn reusable patterns from large collections of sequences, aiming to support tasks such as forecasting, classification, imputation, and anomaly detection across domains. Common approaches include Transformer models with patch or value tokenization, masked and contrastive pretraining, next-token prediction, and newer recurrent or state-space architectures; training data may mix real series with synthetic ones. Models are used zero-shot or adapted through fine-tuning, in-context examples, covariates, retrieval, and lightweight adapters, with benchmarks such as GIFT-Eval and UCR/UEA testing different capabilities. Transfer can reduce the need for task-specific training, but results vary with data and operating conditions: specialists may still win, and robustness to distribution shifts, long contexts, uncertainty, and fair evaluation remain open challenges.

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304 papers69 papers in the last four weeks, up 86% on the four weeks before. 0.4% of all new papers.

CloudCons: A Comprehensive End-to-End Benchmark for Cloud Resource Consolidation
Jun 11, 2026Xiaobin Zhang, Lefei Shen, Mouxiang Chen +6Zero-Shot Time Series Forecasting Forecasting Benchmarks
Zhejiang University Hangzhou, China · State Street Technology (Zhejiang) Ltd. Hangzhou, China · Richoo AI Hangzhou, China +2
CausalMoE: A Billion-Scale Multimodal Foundation Model for Granger Causal Discovery with Pattern-Routed Heterogeneous Experts
Jun 11, 2026Bo Liu, Di Dai, Jingwei Liu +5Time-Series Causal Discovery Mixture-of-Experts Models
State Key Laboratory of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University Beijing, China · National Institute of Health Data Science, and Institute for Artificial Intelligence, Peking University Beijing, China
Time-Series Foundation Model Embeddings for Remaining Useful Life Estimation
Jun 10, 2026Amir El-Ghoussani, Michele De Vita, Ronald Naumann +1RUL Estimation Predictive Maintenance
Friedrich-Alexander-Universität Erlangen–Nürnberg (FAU), Germany · Nokia Solutions and Networks GmbH & Co. KG, Germany
When to Trust, How to Distill: Multi-Foundation Model Guidance for Lightweight, Robust Scientific Time Series Forecasting
Jun 10, 2026Rupasree Dey, Abdul Matin, Nathan Orwick +3Multivariate Time Series Forecasting Time Series Forecasting
Department of Computer Science Colorado State University Fort Collins, Colorado, USA · Department of Soil and Crop Sciences Colorado State University Fort Collins, Colorado, USA
TimeRouter: Efficient and Adaptive Routing of Time-Series Foundation Models
Jun 10, 2026Kanghui Ning, Yushan Jiang, Kashif Rasul +3Adaptive Model Routing Time Series Forecasting
School of Computing, University of Connecticut, Storrs, USA · Department of Machine Learning Research, Morgan Stanley, New York, USA.
APEX: A Network-Native Time-Series Foundation Model for Forecasting and Anomaly Detection for Wireless Edge Operations
Jun 10, 2026Swadhin Pradhan, Niloo Bahadori, Peiman AminiWireless Communications Wireless Foundation Models
Cisco Systems, USA
CITRAS-FM: Tiny Time Series Foundation Model for Covariate-Informed Zero-Shot Forecasting
Jun 9, 2026Yosuke Yamaguchi, Issei Suemitsu, Yuki Kajihara +1Synthetic Data Pretraining Zero-Shot Time Series Forecasting
Research & Development Group, Hitachi Ltd., Tokyo, Japan
UPLOTS: A Unified Pretrained Language Model for Constrained Time-series Generation
Jun 9, 2026Du Yin, Hao Xue, Jinliang Deng +4Constrained Generative Modeling Time Series Generation
University of New South Wales Sydney, NSW, Australia · HKUST(GZ) China · BUAA Beijing, China
LakeFM: Toward a Foundation Model for Aquatic Ecosystems Using Irregular Multivariate Multi-depth Time Series Data
Jun 9, 2026Abhilash Neog, Sepideh Fatemi, Medha Sawhney +9Irregular Time-Series Modeling Multivariate Time Series Forecasting
Virginia Tech Blacksburg, VA, USA · Grand Valley State University Muskegon, MI, USA · University of Wisconsin - Madison Madison, WI, USA +2
Next-Token Prediction Learns Generalisable Representations of Sleep Physiology
Jun 8, 2026Jonathan F. Carter, Lionel TarassenkoSleep Stage Classification Time Series Classification
Institute of Biomedical Engineering University of Oxford
Does Normalization Choice Matter for Causal Large Time-Series Models?
Jun 8, 2026Samy-Melwan Vilhes, Gilles Gasso, Mokhtar Z AlayaTime Series Forecasting Non-Stationary Time Series Forecasting
LMAC · INSA Rouen Normandie, Univ Rouen Normandie Univ, LITIS UR4108, F-76000 Rouen, France · Univ de Technologie de Compi`egne, LMAC EA 222, F-60203 Compi`egne, France
Tyan-WP: A Wind Power Foundation Model for Ultra-Short-Term Probabilistic Forecasting
Jun 7, 2026Jiahui Huang, Ao Luo, Lei Liu +6Zero-Shot Time Series Forecasting Time Series Forecasting
School of Information Science and Technology, University of Science and Technology of China, Hefei, 230022, China · China Electric Power Research Institute, Beijing, 100192, China
Lost in the Non-convex Loss Landscape: How to Fine-tune the Large Time Series Model?
Jun 7, 2026Xu Zhang, Peang Wang, Wei WangFine-Tuning Time-Series Foundation Models
Shanghai Key Laboratory of Data Science College of Computer Science and Artificial Intelligence Fudan University, Shanghai 200082, China
Mix, Don't Pick: Why Synthetic Corpus Composition Matters for Time Series Foundation Model Pretraining
Jun 6, 2026Aaryan Nagpal, Debdeep Sanyal, Murari Mandal +2Synthetic Data Pretraining Time Series Forecasting
Birla AI Labs, Mumbai, India · KIIT, Bhubaneswar, India · BITS Pilani, Pilani, India
Time series Foundation Models based on Physics-Informed Synthetic Histories for Cold-Start Photovoltaic Forecasting
Jun 5, 2026Lorenzo Longarini, Alessandro Rongoni, Simone Silenzi +2Zero-Shot Time Series Forecasting Time Series Forecasting
Sistemi 2000 srl, Civitanova Marche, Italy · Department of Engineering ”Enzo Ferrari”, University of Modena and Reggio Emilia, Modena, Italy · Department of Political Sciences, Communication and International Relations, University of Macerata, Macerata, Italy
GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series
Jun 5, 2026Nick Teutschmann, Laura Crocetti, Fanny Lehmann +2Self-Supervised Time Series Representation Learning Time Series Forecasting
Institute of Geodesy and Photogrammetry, ETH Zurich, Switzerland · Institute of Geodesy and Photogrammetry, ETH Zurich, Zurich, Switzerland · ETH AI Center, Switzerland +1
BCG-FM: A Foundation Model for Ambient Cardiac Health Sensing
Jun 5, 2026Magnus Ruud Kjaer, Haejun Han, Ashish Neupane +1Time-Series Foundation Models
AI/ML, Eight Sleep · AI/ML, Eight Sleep (work done while at Eight Sleep)
GlucoFM-Bench: Benchmarking Time-Series Foundation Models for Blood Glucose Forecasting
Jun 5, 2026Baiying Lu, Zhaohui Liang, Ryan Pontius +2Zero-Shot Time Series Forecasting Time Series Forecasting
Department of Computer Science Dartmouth College Hanover, NH 03784 · Department of Computer Science Emory University Atlanta, GA 30322 · Quantitative Biomedical Sciences Dartmouth College Hanover, NH 03784
TRACE: A Temporal Conditional Estimation for Multimodal Time Series Foundation Models
Jun 4, 2026Ziwen Kan, Yishuo Chen, Kecheng Li +7Irregular Time-Series Modeling Missing-Modality Learning
Department of Computer Science, University of Central Florida · Department of Computer Science, University of North Carolina at Chapel Hill
TS-ICL: A Flexible Time-Indexed Foundation Model for Time Series via In-Context Learning
Jun 4, 2026Etienne Le Naour, Tahar Nabil, Adrien PetraliaIrregular Time-Series Modeling Time Series Imputation
1EDF R&D, Palaiseau, France
GITCO: Gated Inference-Time Context Optimization in TSFMs
Jun 3, 2026Manya Pandey, Dhruv Kumar, Murari Mandal +1Zero-Shot Time Series Forecasting Inference-Time Optimization
Birla AI Labs, Mumbai, India · KIIT, Bhubaneswar, India · BITS Pilani, Pilani, India
TimeBlocks: Foundational and Continual Time-Series Blockbase -- Extended Version
Jun 1, 2026David Campos, Bin Yang, Tung Kieu +3Continual Test-Time Adaptation Multi-Task Learning
Aalborg University, Denmark · University of Amsterdam Netherlands · East China Normal University China +1
Time Series as Language: A Universal Tokenizer for General-Purpose Time Series Foundation Models
May 31, 2026Yunhao Zhang, Ruiying Qi, Jiale Zheng +3Time Series Tokenization Time Series Generation
Shanghai Jiao Tong University · Huawei Noah’s Ark Lab
GlucoFM: A Dual-Stream Foundation Model for Continuous Glucose Monitoring
May 29, 2026Zechen Li, Keerthana Natarajan, Weizhi Zhang +11Irregular Time-Series Modeling Latent Dynamics Modeling
Google Research · Work done during an internship at Google · University of New South Wales, Sydney
EVL-ECG: Efficient ECG Interpretation With Multi-Aspect Heterogeneous Knowledge Distillation
May 28, 2026Dang Nguyen Hong, Nhi Ngoc-Yen Nguyen, Huy-Hieu PhamEfficient Neural Network Inference Electrocardiogram Foundation Models
Hanoi University of Science and Technology, Hanoi, Vietnam · VinUni-Illinois Smart Health Center, VinUniversity, Hanoi, Vietnam · College of Engineering and Computer Science, VinUniversity, Hanoi, Vietnam +1
Rethinking Post-Training Recipes for Multimodal Time-Series Forecasting
May 28, 2026Haoxin Liu, Yichen Zhou, Rajat Sen +2Multivariate Time Series Forecasting LLM Post-Training
Georgia Institute of Technology · Google Research
Falcon-X: A Time Series Foundation Model for Heterogeneous Multivariate Modeling
May 26, 2026Yiding Liu, Yifan Hu, Hongjie Xia +5Multivariate Time Series Forecasting Zero-Shot Time Series Forecasting
Towards Continuous-time Causal Foundation Models
May 26, 2026Dennis Thumm, Ruben Wiedemann, Ying ChenIrregular Time-Series Modeling Causal Foundation Models
National University of Singapore · Department of Mathematics, Centre for Quantitative Finance, Risk Management Institute, National University of Singapore. · Imperial College London
Unicorn: Scaling High-Dimensional Time Series Forecasting via Universal Correlation Modeling
May 26, 2026Haochen Yuan, Yichen Song, Yunbo Wang +1Multivariate Time Series Forecasting Time-Series Foundation Models
MoE Key Lab of Artificial Intelligence, AI Institute, School of Computer Science Shanghai Jiao Tong University
A Signal-Language Foundation Model for Broad-Spectrum Cardiovascular Assessment from Routine Electrocardiography
May 25, 2026Ziqing Yu, Yuhui Tao, Jiayu Huo +19Electrocardiogram Classification Multimodal Contrastive Learning
Department of Cardiology, Zhongshan Hospital of Fudan University, Shanghai Institute of Cardiovascular Diseases, National Clinical Research Centre for Interventional Medicine, Shanghai, China · Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, 200032, China. · Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai, 200032, China. +8

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CloudCons: A Comprehensive End-to-End Benchmark for Cloud Resource Consolidation

Xiaobin Zhang, Lefei Shen, Mouxiang Chen, Zhuo Li+5

Zhejiang University Hangzhou, China · State Street Technology (Zhejiang) Ltd. Hangzhou, China · Richoo AI Hangzhou, China+2

Zero-Shot Time Series Forecasting Forecasting Benchmarks Time-Series Foundation Models Probabilistic Time Series Forecasting

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Driven by conservative over-provisioning to guarantee service reliability, resource utilization in cloud data centers remains at low levels. To mitigate this, the forecast-then-optimize paradigm has emerged to optimize consolidation by anticipating future demands. While emerging time series foundation models promise to enhance this paradigm through zero-shot generalization, existing benchmarks focus solely on prediction error metrics. The actual decision utility of these advanced models remains unverified, rendering their practical value for downstream tasks uncertain. To bridge this gap, we propose CloudCons, a comprehensive end-to-end benchmark designed to evaluate forecasting models within the specific context of cloud resource consolidation. We build high-quality datasets that cover diverse workloads from Huawei Cloud, Microsoft Azure, and Google Borg, capturing distinct service characteristics ranging from synchronized diurnal rhythms to stochastic, pulse-like bursts and high-frequency noise. We conduct an extensive evaluation of statistical, deep learning, and foundation models. Our experiments reveal a pivotal finding: while foundation models demonstrate superior zero-shot forecasting accuracy, this advantage does not inherently translate into better decision utility. Of practical significance, we systematically analyze how the selection of predictive quantiles acts as a critical lever. We provide actionable guidelines for calibrating these selections to balance the trade-off between resource efficiency and service reliability, offering vital insights for real-world deployment decisions.