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.

AME-TS: Anchored Mixture-of-Experts for Time Series Forecasting
May 24, 2026Rui Wang, Renhao Xue, Ray Razi +2Sparse Mixture-of-Experts Time Series Forecasting
Amazon Web Services
TSFMAudit: Data Contamination Auditing in Forecasting Time Series Foundation Models
May 24, 2026Hongkai Li, Shifeng Xie, Lefei Shen +7Data Contamination Time-Series Foundation Models
Zhejiang University · Télécom Paris · State Street Technology (Zhejiang) Ltd. +1
ROAR: Retrieval Opportunity-Aware Refinement for Zero-Shot Time Series Forecasting
May 24, 2026Jinjin Chi, Lei Feng, Yiming Wang +4Zero-Shot Time Series Forecasting Time Series Forecasting
College of Computer Science and Technology, Jilin University, Changchun, China · College of Computing and Data Science, Nanyang Technological University, Singapore
Assessing the Operational Viability of Foundation Models for Time Series Forecasting
May 23, 2026Kavin Soni, Debanshu Das, Vamshi GuduguntlaZero-Shot Time Series Forecasting Time Series Forecasting
Google, USA
Continuity and Ordinality Matter: Constraining Time Series Tokens for Effective Time Series Analysis with Large Language Models
May 22, 2026Musheng Li, Ziying Zhang, Cheng jin +1Time Series Forecasting Time Series Representation Learning
Department of Electronic Engineering Tsinghua University
ChronoVAE-HOPE: Beyond Attention -- A Next-Generation VAE Foundation Model for Specialized Time Series Classification
May 21, 2026José Alberto Rodríguez, Luis Balderas, Miguel Lastra +2Variational Autoencoders Disentangled Representation Learning
Department of Computer Science and Artificial Intelligence DiCITS, iMUDS, DaSCI University of Granada, Granada, Spain 18071 · Advanced Medical Imaging Group, Instituto de Investigación Biosanitaria de Granada (ibs.Granada) · Department of Software Engineering DiCITS, iMUDS, DaSCI University of Granada, Granada, Spain 18071 +1
Toto 2.0: Time Series Forecasting Enters the Scaling Era
May 19, 2026Emaad Khwaja, Chris Lettieri, Gerald Woo +10Time Series Forecasting Hyperparameter Transfer
Datadog AI Research · Carnegie Mellon University
Chronicle: A Multimodal Foundation Model for Joint Language and Time Series Understanding
May 18, 2026Paul Quinlan, Jeremy Levasseur, Qingguo Li +1Cross-Modal Learning Multivariate Time Series Forecasting
1InertialAI · Department of Electrical and Computer Engineering, Queen’s University · Department of Mechanical and Materials Engineering, Queen’s University
KairosHope: A Next-Generation Time-Series Foundation Model for Specialized Classification via Dual-Memory Architecture
May 18, 2026Luis Balderas, José Alberto Rodríguez, Miguel Lastra +2Memory-Augmented Neural Networks Time Series Classification
Department of Computer Science and Artificial Intelligence · DiCITS, iMUDS, DaSCI · Advanced Medical Imaging Group, Instituto de Investigación Biosanitaria de Granada (ibs.Granada) +3
Olivia: Harmonizing Time Series Foundation Models with Power Spectral Density
May 17, 2026Jingru Fei, Kun Yi, Alex Xing Wang +3Zero-Shot Time Series Forecasting Frequency-Domain Feature Learning
Beijing Institute of Technology · North China Institute of Computing Technology · State Information Center +4
How Do Electrocardiogram Models Scale?
May 17, 2026Jiawei Li, Fabio Bonassi, Ming Jin +4Self-Supervised Pre-Training Electrocardiography
Uppsala University · Griffith University
Empirical evaluation of Time Series Foundation Models for Day-ahead and Imbalance Electricity Price Forecasting in Belgium
May 16, 2026Chi Bui, Maria Margarida Mascarenhas, Arnaud Verstraeten +1Zero-Shot Time Series Forecasting Electricity Price Forecasting
ELECTA-ESAT (KU Leuven) Leuven, Belgium · ELECTA-ESAT(KU Leuven) and Gridual Leuven, Belgium
Extending Pretrained 10-Second ECG Foundation Models to Longer Horizons
May 16, 2026Wei Tang, Jinpei Han, Kangning Cui +10Electrocardiography Electrocardiogram Foundation Models
1City University of Hong Kong · 2Imperial College London · 3Wake Forest University +3
TopoPrimer: The Missing Topological Context in Forecasting Models
May 14, 2026Zara Zetlin, Kayhan Moharreri, Maria SafiTime Series Forecasting Sheaf Theory
Apple
NeuroAtlas: Benchmarking Foundation Models for Clinical EEG and Brain-Computer Interfaces
May 14, 2026Konstantinos Kontras, Trui Osselaer, Stylianos G. Mouslech +12Electroencephalography Brain-Computer Interfaces
KU Leuven · MIT
DT-Transformer: A Foundation Model for Disease Trajectory Prediction on a Real-world Health System
May 14, 2026Yunying Zhu, Andrew R Weckstein, Kueiyu Joshua Lin +1Clinical Prediction Time Series Transformers
Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA · Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA · Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, MA, USA +2
SurF: A Generative Model for Multivariate Irregular Time Series Forecasting
May 13, 2026Mohammad R. Rezaei, Tejas Balaji, Rahul G. KrishnanIrregular Time-Series Modeling Multivariate Time Series Forecasting
Department of Computer Science University of Toronto Vector Institute Toronto, ON, Canada
Dynamical Predictive Modelling of Cardiovascular Disease Progression Post-Myocardial Infarction via ECG-Trained Artificial Intelligence Model
May 13, 2026Riccardo Cavarra, Lupo Lovatelli, Shaheim Ogbomo-Harmitt +4Electrocardiography Clinical Outcome Prediction
King’s College London, St Thomas’ Hospital, London, UK · North Bristol NHS Trust, Bristol, UK
Compact Latent Manifold Translation: A Parameter-Efficient Foundation Model for Cross-Modal and Cross-Frequency Physiological Signal Synthesis
May 13, 2026Bo Cui, Xiaowen Song, Yaowen Zhang +4Time-Series Foundation Models Multimodal Learning
Department of Biomedical Signals and Systems University of Twente · Department of Applied Mathematics University of Twente
ISOMORPH: A Supply Chain Digital Twin for Simulation, Dataset Generation, and Forecasting Benchmarks
May 12, 2026Zhizhen Zhang, Hyemin Gu, Benjamin J. Zhang +6Zero-Shot Time Series Forecasting Forecasting Benchmarks
University of Massachusetts Amherst · University of North Carolina · SRI International +1
Pretraining Strategies and Scaling for ECG Foundation Models: A Systematic Study
May 12, 2026M A Al-Masud, Nils StrodthoffElectrocardiogram Foundation Models Self-Supervised Time Series Representation Learning
AI4Health Division Carl von Ossietzky Universität Oldenburg Oldenburg, Lower Saxony, Germany
Investigating simple target-covariate relationships for Chronos-2 and TabPFN-TS
May 12, 2026Gaspard Berthelier, Mariia Baranova, Andrei-Tiberiu Pantea +4Multivariate Time Series Forecasting Time Series Forecasting
EDF R&D, Palaiseau · Inria Sophia-Antipolis, Université Côte d’Azur · Université Paris Cit'e
Efficient and Adaptive Human Activity Recognition via LLM Backbones
May 12, 2026Aleksandr Bredikhin, Philippe Lalanda, German VegaHuman Activity Recognition Domain Generalization
Univ. Grenoble Alpes, France
Developing a foundation model for high-resolution remote sensing data of the Netherlands
May 11, 2026Paul Vermeeren, Heysem KayaRemote Sensing Image Understanding Geospatial Foundation Models
Department of Information and Computer Sciences, Utrecht Univ., the Netherlands
TimeClaw: A Time-Series AI Agent with Exploratory Execution Learning
May 11, 2026Hangchen Liu, Dongyuan Li, Renhe Jiang +3Time Series Reasoning LLM-Based Time Series Forecasting
The University of Tokyo · Southern University of Science and Technology
TokaMind for Power Grid: Cross-Domain Transfer from Fusion Plasma
May 10, 2026JC Wu, Norton Lee, Kai Siang ChenTime Series Classification Critical Slowing Down
TaiScience Research Group, affiliated with Fu Jen Catholic University, Taiwan · Center for Geometry and Physics, Institute for Basic Science (IBS), South Korea · Fu Jen Catholic University, Taiwan
Biosignal Fingerprinting: A Cross-Modal PPG-ECG Foundation Model
May 10, 2026Zhangdaihong Liu, Chang Liu, Fenglin Liu +4Cross-Modal Representation Learning Masked Autoencoders
Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, UK · Oxford Suzhou Centre for Advanced Research, Suzhou, 215123, China · School of Public Health, Shanghai Jiao Tong University, Shanghai, China
WavesFM: Hierarchical Representation Learning for Longitudinal Wearable Sensor Waveforms
May 9, 2026Peng Cao, Zhijian Yang, Tennison Liu +17Self-Supervised Time Series Representation Learning Hierarchical Representation Learning
Google Research · University of Washington · MIT
FactoryNet: A Large-Scale Dataset toward Industrial Time-Series Foundation Models
May 9, 2026Jonas Petersen, Pratik Jawahar, Riccardo Maggioni +11Manufacturing Industrial Anomaly Detection
Forgis · ETH Zurich · Cairo University +3
Event Fields: Learning Latent Event Structure for Waveform Foundation Models
May 9, 2026Li Na, Yuanyun Zhang, Shi LiIrregular Time-Series Modeling Latent Dynamics Modeling
University of Science and Technology of China · Columbia University

May 24, 2026J/K move · Enter open · S save

AME-TS: Anchored Mixture-of-Experts for Time Series Forecasting

Rui Wang, Renhao Xue, Ray Razi, Huan Song+1

Amazon Web Services

Sparse Mixture-of-Experts Time Series Forecasting Expert Routing Time-Series Foundation Models Time Series Modeling

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Time series forecasting models are increasingly scaled through large Transformer backbones, yet most existing approaches process all series through a shared dense computation path despite substantial heterogeneity in temporal structure. Mixture-of-Experts (MoE) offers a natural alternative by enabling conditional computation, but standard MoE routing leaves expert specialization weakly identified and often unstable during downstream adaptation. We propose AME-TS, a structure-guided sparse time series foundation model that aligns expert routing with interpretable temporal structure. AME-TS first uses a lightweight regime predictor to estimate series-level descriptors, including forecastability, seasonality, trend, and sparsity, and maps them to a soft structural prior over experts. This series-level prior guides token-level routing during training, encouraging structure-aligned specialization. On the GIFT-Eval benchmark, AME-TS delivers a strong accuracy-efficiency tradeoff across model scales: it substantially outperforms existing time series foundation models at small model scales and remains competitive with the strongest models at larger scales, while activating substantially fewer parameters through sparse routing. We further show that AME-TS learns more interpretable routing geometry and substantially more stable expert specialization than standard MoE during fine-tuning on the M5 dataset. These results suggest that structure-aware routing is an effective and reliable way to realize the benefits of sparse expert models for time series forecasting.