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.

Scale-Aware Pretraining of Time Series Foundation Models via Multi-Patch Token Alignment and Hybrid Masking
Aug 20, 2026Taihua Chen, Xiang Ma, Yixin Zhang +3Time-Series Foundation Models Self-Supervised Pre-Training
School of Software, Shandong University, Jinan 250101, China · Joint SDU–NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan 250101, China · Nanyang Technological University, Singapore
LiveHouse-TS: An Open-world Living Benchmark for Time Series Foundation Models
Aug 18, 2026Haomin Wen, Ziyu Zhou, Qingxiang Liu +2Forecasting Benchmarks Time-Series Foundation Models
Shanghai Innovation Institute · The Hong Kong University of Science and Technology (Guangzhou)
Forecast Collapse in Time-Series Foundation Models
Aug 14, 2026Shu Wan, Miles Ma, Hank Zhu +4Time Series Forecasting Time-Series Foundation Models
Abel AI Lab · Arizona State University · University of Oxford
Model-agnostic Retrieval-Augmented Extended Forecasting for time series
Aug 14, 2026Juan Pablo Villa Serna, Rohan Asthana, Vasileios BelagiannisTime Series Forecasting Retrieval-Augmented Time Series Forecasting
Friedrich-Alexander-Universität Erlangen–Nürnberg
Comparative Analysis of State-of-the-Art Foundation Models for Sleep Analysis Under Channel Reduction
Aug 14, 2026Hassan Mehdi, Riku Klen, Ayse Kosal Bulbul +5Sleep Stage Classification EEG Foundation Models
Institute of Biomedicine, University of Turku, Finland · Turku PET Centre, University of Turku and Turku University Hospital, Finland · University College Cork, Ireland +3
GALA: Generation-Aware Cross-Modal Alignment for Text-to-Time-Series Synthesis
Aug 13, 2026Haochen Zhang, Gengwei Zhang, Laura Yao +2Time Series Generation Conditional Generative Modeling
UNITES Lab, University of North Carolina at Chapel Hill
Foundation models for movement data: Are they ready for prime-time?
Aug 13, 2026Alexander Bräuer, Benjamin Cauchi, Nils StrodthoffHuman Activity Recognition Healthcare
Carl von Ossietzky Universität Oldenburg, Oldenburg, Lower Saxony, 26129, Germany
Into the ORBIT for Time Series: Training Regimes for Foundation Models
Aug 13, 2026Hongjie Xia, Yiding Liu, Yifan Hu +2Zero-Shot Time Series Forecasting Self-Supervised Pre-Training
Market-Information-Aware Gated-LoRA of Foundation Models for Transferable Day-Ahead Electricity Price Forecasting
Aug 11, 2026Hang Fan, Wei Wei, Shengwei MeiElectricity Price Forecasting Time Series Forecasting
Hybrid Neural-Classical Correction for Frozen Time Series Foundation Models: A Comprehensive Ablation Study on High-Frequency Stock Prediction
Aug 9, 2026Kasun Dewage, Suranadi De Silva, Shankhadeep MondalResidual Learning Time Series Forecasting
Dept. of Mathematics University of Central Florida Orlando, FL, USA · Dept. of Computer Science University of Central Florida Orlando, FL, USA
Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models
Aug 8, 2026Jianqi Zhang, Xingyu Zhang, Zeen Song +3Time Series Forecasting RL Post-Training
Institute of Software, Chinese Academy of Sciences, Beijing, China
Align-RAG: Alignment Is All You Need for TSFM In-Context Learning
Aug 6, 2026Mohammad Asadi, Soheil Hor, Bardiya Akhbari +6Zero-Shot Time Series Forecasting Retrieval-Augmented ICL
1Stanford University 2Amazon
MarsCast: Transfer Learning of AI Weather Foundation Models to Planetary Atmospheres
Aug 5, 2026M. L. Carroll, J. Li, S. D. Guzewich +3Transfer Learning Time-Series Foundation Models
1) NASA Goddard Space Flight Center · 2) ADNet Systems, Inc.
Personalized Federated Sparse Adaptation of Time-Series Foundation Models
Aug 5, 2026Priyanka Nihalchandani, Naman Srivastava, Varun Ojha +1Mixture of Experts Time Series Forecasting
Robert Bosch Centre for Cyber-Physical Systems, Indian Institute of Science, Bengaluru, India · School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
FinVerse: Toward More Realistic Evaluation for Financial Time-Series Forecasting
Aug 4, 2026Jaehoon Lee, Jun Seo, Seunghan Lee +17Benchmark Design Financial Forecasting
LG AI Research
CENTILE: A Telemetry Foundation Model Evaluated by the Decisions It Drives
Aug 3, 2026Zifan Zhang, Zhichao Hou, Tingxiang Ji +1High-Performance Computing Time Series Forecasting
Department of Computer Science, North Carolina State University, Raleigh, NC 27695, USA
FedChronos: Federated Fine-Tuning of Time-Series Foundation Models for Privacy-Preserving Commodity Price Forecasting
Aug 2, 2026Amit Sharma, Nitin Auluck, Akramul AzimTime Series Forecasting Federated Fine-Tuning
Indian Institute of Technology Ropar, Punjab, India · Ontario Tech University, Oshawa, Canada
TFGformer: Multivariate Time Series Forecasting via Time-Frequency Graph Learning and Covariate Fusion
Jul 31, 2026Yu Sun, Yuan Chang, Xiaohou Shi +1Multivariate Time Series Forecasting Long-Term Time Series Forecasting
Beijing University of Posts and Telecommunications Beijing, China · China Telecom Research Institute Beijing, China
LLM as Forecasting Planner: Training-Free Text Conditioning for Time-Series Foundation Models
Jul 27, 2026Huu Hiep Nguyen, Dung Nguyen, Minh Hoang Nguyen +2Time Series Forecasting LLM-Based Time Series Forecasting
Applied Artificial Intelligence Initiative Deakin University Geelong, Australia
CAPT: A Multi-task Continuous Autoregressive Transformer enabling Cross-dataset and Cross-species Transfer for Calcium Population Dynamics
Jul 25, 2026Xinhong Xu, Yimeng Zhang, Yuanlong ZhangTransformer Neural Decoding
Department of Automation, Tsinghua University, Beijing, China · School of Life Sciences, Tsinghua University, Beijing, China
Foundation Models and Fine-Tuning: Toward a New Generation of Models for Time Series Forecasting
Jul 25, 2026Morad Laglil, Bertrand Pracca, Emilie Devijver +1Zero-Shot Time Series Forecasting Time-Series Foundation Models
Savoye, 18 bd des Gorgets, 21000, Dijon, France · Univ. Grenoble Alpes, CNRS, Grenoble INP, LIG, Grenoble, 38000, France
Zero-Shot Heart Rate Variability Forecasting from Consumer Wearables Using Time Series Foundation Models
Jul 22, 2026Luukas Peräkylä, Fahad Sohrab, Ville Hautamäki +3Zero-Shot Time Series Forecasting Time Series Forecasting
Tampere University, Tampere, Finland · University of Eastern Finland, Finland
Post-Training in Time Series Foundation Models: A Unifying Framework
Jul 22, 2026Shifeng Xie, Ambroise Odonnat, Zehao Xiao +7Model Compression Time-Series Foundation Models
Noah’s Ark Lab · Université Paris Cité · Inria +2
Expert-Guided Forecast Editing for Time-Series Foundation Models
Jul 22, 2026Hung Le, Minh Hoang Nguyen, Manh Nguyen +2Black-Box Optimization Time Series Forecasting
Deakin Applied Artifical Intelligence Initiative Deakin University, Australia
Towards Reliable Zero-Shot Crowd Forecasting: Evaluating Time Series Foundation Models for Special Event Pedestrian Forecasting
Jul 20, 2026Ziteng Li, Yanan Xin, Tina Comes +1Zero-Shot Time Series Forecasting Spatiotemporal Forecasting
Delft University of Technology (TU Delft), Department of Transport & Planning, Delft, The Netherlands. · Delft University of Technology (TU Delft), Technology, Policy and Management, Delft, The Netherlands. · German Aerospace Center (DLR), Institute for the Protection of Terrestrial Infrastructures, Sankt Augustin, Germany.
Lightweight Wrappers for Adapting Time Series Foundation Models to Regional Drought Forecasting
Jul 20, 2026Wentao Gao, Jiuyong Li, Lin Liu +4Test-Time Adaptation Time Series Forecasting
Adelaide University · Adelaide, Australia · CSIRO Technology +1
Residual-Guided Multi-Resolution Refinement of Foundation Models: A Case Study in Drought Forecasting
Jul 20, 2026Wentao Gao, Jiuyong Li, Lin Liu +4Time Series Forecasting Climate Modeling
Adelaide University, Adelaide, SA, Australia · CSIRO Technology, Canberra, Australia · CSIRO Environment, Canberra, Australia
A Benchmark for Electrical Load Forecasting Across Grid Levels: Time-Series Transformers Outperform Established Methods
Jul 17, 2026Matthias Hertel, Sebastian Pütz, Jonathan Kolar +3Time Series Forecasting Forecasting Benchmarks
Karlsruhe Institute of Technology, Germany · Helmholtz AI, Germany
A Minimal Interpretable Architecture for Zero-Shot Reconstruction of Dynamical Systems
Jul 16, 2026Christoph Jürgen Hemmer, Florian Plaswig, Daniel DurstewitzDynamical Systems Zero-Shot Time Series Forecasting
Dept. of Theoretical Neuroscience, Central Institute of Mental Health, Mannheim, Germany · Interdisciplinary Center for Scientific Computing (IWR), Heidelberg, Germany · Faculty of Physics and Astronomy, Heidelberg University, Heidelberg, Germany
VLT: A Vision-Language-Time Series Multimodal Foundation Model for Industrial Intelligence
Jul 16, 2026Haiteng Wang, Jingheng Yan, Xiaokang Wang +1Representation Learning Predictive Maintenance
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China · School of Computer Science and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China · School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China, also with the Hangzhou International Innovation Institute, Beihang University, Hangzhou 311115, China, and also with the State Key Laboratory of Intelligent Manufacturing System Technology, Beijing 100854, China

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Scale-Aware Pretraining of Time Series Foundation Models via Multi-Patch Token Alignment and Hybrid Masking

Taihua Chen, Xiang Ma, Yixin Zhang, Tailin Zhan+2

School of Software, Shandong University, Jinan 250101, China · Joint SDU–NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan 250101, China · Nanyang Technological University, Singapore

Time-Series Foundation Models Self-Supervised Pre-Training Multiscale Time-Series Modeling Multivariate Time Series Forecasting

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Pretraining time series foundation models across heterogeneous datasets necessitates effective handling of varying sampling frequencies. Current methods either employ dataset-specific patch sizes and separate FFNs, leading to fragmented representations, or enforce a fixed patch size that neglects inherent temporal variations. To address this, we propose SATS, featuring a scale-aware token alignment mechanism that treats patch size as an explicit notion of scale. By incorporating a contrastive-inspired alignment regularizer, SATS aligns representation spaces across scales while preserving distinct modeling capacities. Furthermore, a hybrid masking strategy combining random and contiguous masking is introduced to capture multi-scale temporal structures. Experimental results on LSTF benchmarks demonstrate that SATS achieves a 9.2% improvement in MSE and an 8.3% gain in GIFT-Eval MASE compared to competitive baselines. Notably, SATS consistently delivers SOTA performance while achieving a 65.6% increase in model efficiency over advanced baselines, highlighting its effectiveness and scalability in time series pretraining.