Time Series Foundation Models

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29 papers in the last four weeks, up 625% on the four weeks before. 0.3% of all new papers.

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Attenuated in-context identification in time-series foundation models: diagnosis under counterfactual inputs and repair by synthetic forced-system fine-tuning
Oct 6, 2026Hong-In WonTime Series Foundation Models
Manufacturing AI Research Center, Korea Institute of Industrial Technology, Incheon, Republic of Korea · HYU-KITECH Joint Department, Graduate School, Hanyang University, Seoul, Republic of Korea
Scale-Invariant Training for Time Series Foundation Models
Oct 5, 2026Ignacy Stepka, Willa Potosnak, Kin G. Olivares +1Time Series Foundation Models Wireless Foundation Models
Carnegie Mellon University · Amazon · Nixtla
Normality Constraint Learning: Adapting Foundation Models for Time Series Anomaly Detection
Oct 5, 2026Xiaohui Zhou, Yijie Wang, Hongzuo Xu +2Time-Series Anomaly Detection Time Series Foundation Models
National Key Laboratory of Parallel and Distributed Computing · College of Computer Science and Technology, National University of Defense Technology · Intelligent Game and Decision Lab (IGDL) +1
Time-series Foundation Models for Predictive Control: The Role of Excitation
Oct 5, 2026Mazen Amria, Jasper Hoffmann, Philipp Bordne +5Time Series Foundation Models Wireless Foundation Models
University of Freiburg, Germany · Norwegian University of Science and Technology, Norway
Anlu: Enabling In-Context Time Series Anomaly Detection in Foundation Models via Counterfactual Supervision
Oct 5, 2026Tian Lan, Yifei Gao, Yimeng Lu +5Time-Series Anomaly Detection Time Series Foundation Models
Department of Industrial Engineering, Tsinghua University · Huawei
What Does an Observability Foundation Model Know?
Oct 4, 2026Dhyey Dharmendrakumar Mavani, Rian Atri, Tairan JiTime Series Foundation Models
Independent · Keiji AI
Rethinking Tabular Foundation Models On Data Streams
Oct 4, 2026Nilesh Verma, Daniel Nowak-Assis, Afonso Lourenço +4Tabular Foundation Models Time Series Foundation Models
AI Institute, University of Waikato, New Zealand · LIP6, CNRS, Sorbonne Universit´e, France · GECAD, Polytechnic of Porto, Portugal
A Unified Scaling Law for Time Series Foundation Models
Oct 4, 2026Xilin Dai, Yiding Liu, Zewei Dong +2Time Series Foundation Models Wireless Foundation Models
Ant International · The Chinese University of Hong Kong
Pythia: Toward Foundation World Models for Multimodal Time Series
Oct 4, 2026Xilin Dai, Hongzhou Chen, Yifan Hu +3Time Series Foundation Models Multimodal Foundation Model
Ant International
Foundations without Fundamentals: Zero-Shot Blind Spots in Time Series FMs
Oct 1, 2026Nafiseh Ghoroghchian, Haipeng Zhang, Shuyi Han +2Time Series Foundation Models Zero-Shot Forecasting
Layer6 AI
Generalist Representation, Specialist Detection: TS-Router for Time-Series Anomaly Detection
Oct 1, 2026Tian Lan, Yifei Gao, Yimeng Lu +6Time-Series Anomaly Detection Graph Anomaly Detection
Department of Industrial Engineering Tsinghua University · Huawei · Datadog AI Research Paris, France
Grounding Time-Series Foundation Models in Digital Twin Topology for Predictive Maintenance
Sep 30, 2026Sizhe Ma, Katherine A. Flanigan, Mario BergésTime Series Foundation Models Predictive Maintenance
Department of Civil & Environmental Engineering, Carnegie Mellon University 5000 Forbes Ave., Pittsburgh, PA USA 15213
A Comprehensive Benchmark of Source-Free Universal Domain Adaptation on Time Series Representations
Sep 30, 2026Romain Mussard, Fannia Pacheco, Maxime Berar +2Domain Adaptation Time Series Foundation Models
Univ Rouen Normandie, INSA Rouen Normandie, Universit´e Le Havre Normandie, Normandie Univ, LITIS UR 4108, F-76000 Rouen, France
RACE: Residual-Aware Test-Time Adaptation for Neighbor-Rich Time-Series Foundation Model Forecasting
Sep 30, 2026Hao-Nan Shi, Tong Wu, Chen-Cong Sun +3Time Series Foundation Models Continuous Glucose Monitoring
School of Artificial Intelligence, Nanjing University · National Key Laboratory for Novel Software Technology, Nanjing University · Nanjing, China
Raw-Routed Mixture of Adapters: A Causal Intervention for Routing Collapse in Time Series Foundation Models
Sep 30, 2026Hung Phan, Thuy T. Nguyen, Minh Ngoc Dinh +1Time Series Foundation Models Causal Intervention
When, Not How Much: Evaluating Time-Series Foundation Models on Sparse Events
Sep 30, 2026Daniel Schoess, Florian von WangenheimTime Series Foundation Models Data-Driven Forecasting
ETH Zurich
Loss-Guided Pretraining Data Selection for Time-Series Foundation Models
Sep 29, 2026Yike Li, Shaoxu Song, Jianmin WangTime Series Foundation Models Pretraining
Tsinghua University
QiYao-M: Multimodal Time Series Foundation Model with Role-Aware Modeling of Endogenous and Exogenous Modalities
Sep 28, 2026Hanyin Cheng, Linfeng Wang, Zhengbo Qu +7Time Series Foundation Models Multimodal Classification
East China Normal University · Huawei Technologies Co., Ltd.
Instance-Adaptive Prompts as Context for Time-Series Foundation Models
Sep 28, 2026Zehao Xiao, Shifeng Xie, Lei Zan +5Time Series Foundation Models Wireless Foundation Models
Huawei Noah’s Ark Lab, Paris, France · LIPADE, Universit´e Paris Cit´e, Paris, France · Huawei Noah’s Ark Lab, Shenzhen, China
KiT: A Foundation Model for Financial Time-Series Forecasting using DiffusionTransformers
Sep 28, 2026Boyu Zhang, Haorui LiTime Series Foundation Models Time Series Forecasting
University of California, Los Angeles · Southeast University
Dynamical Parameters: An Interpretability Framework for Time-Series Foundation Models
Sep 28, 2026Kang Yang, Gaofeng Dong, Liying Han +1Time Series Foundation Models Hidden States
Department of Electrical and Computer Engineering University of California, Los Angeles
BITS: Rethinking Fair and Comprehensive Evaluation for Irregular Time Series Forecasting
Sep 27, 2026Kangjia Yan, Linfeng Wang, Tianen Shen +7Time Series Time Series Foundation Models
East China Normal University, Shanghai, China. · University of Electronic Science and Technology of China, Chengdu, China. · Aalborg University, Aalborg, Denmark.
SGA: Uncertainty Quantification for Multi-Step Forecasting in Time Series Foundation Models
Sep 23, 2026Xin-Yu Hu, Shuang Liang, Cheng Feng +1Retrieval-Augmented Forecasting Time Series Foundation Models
National Key Laboratory for Novel Software Technology, Nanjing University, China. · School of Intelligent Science and Technology, Nanjing University, China. · Siemens Data and AI Research, Beijing, China. +1
Time-Series Foundation Models That Understand Data Revisions
Sep 23, 2026Taimoor AhmadTime Series Foundation Models Reproducibility
Superior University Lahore, Pakistan
Interweaving Marginals into Multivariate Sample Paths: Training-Free Dependence Construction for Probabilistic Time Series Foundation Models
Sep 22, 2026Jinmyeong Choi, Jinkwan Jang, Seul Lee +1Multivariate Time Series Forecasting Time Series Foundation Models
Graduate School of Data Science Seoul National University
$t_0$ : A Time-Series Foundation Model for Forecasting with Context
Sep 21, 2026Lucas Meyer, Claudio Sole, Huikan Xiang +6Time Series Foundation Models Probabilistic Forecasting
The Forecasting Company · Macrocosm
Leveraging Industrial Foundation Models at the Edge of Particle Physics Detectors via Distillation Learning and Hardware Co-design
Sep 20, 2026Gia Ancone, Qibin Liu, Liangyu Wu +1Wireless Foundation Models Time Series Foundation Models
Department of Physics Stanford University 450 Jane Stanford Way, Stanford, CA 94305, United States of America · SLAC National Accelerator Laboratory 2575 Sand Hill Road, Menlo Park, CA 94025, United States of America
A Hybrid Attention Model Learning Unified Time-aware Patch Representation for Irregular Multivariate Time Series Forecasting
Sep 19, 2026Li Lin, Zhihao Lin, Qi Zhang +3Time Series Foundation Models
Southeast University, China · Nanyang Technological University, Singapore · Timecho Ltd., China
TabPFN-3.5: Technical Report
Sep 15, 2026Benjamin Jäger, Nick Erickson, Léo Grinsztajn +44Tabular Foundation Models Time Series Foundation Models
SOTER: A Generative Time-Series Foundation Model for Wearable Human Physiological Signals
Sep 15, 2026Fangke Chen, Sirry Chen, Wei Chen +1Wearable Physiology Time Series Foundation Models
School of Integrated Circuits, Zhejiang University Hangzhou, China · Shanghai Innovation Institute Shanghai, China · School of Data Science, Fudan University Shanghai, China +1
How Good Are Time-Series Foundation Models for Pedestrian Crowd Count Forecasting? A Cross-Dataset Comparative Study
Sep 14, 2026Theivaprakasham Hari, Ziteng Li, Yanan Xin +2Pedestrian Trajectory Prediction Time Series Foundation Models
Department of Transport & Planning, Faculty of Civil Engineering and Geosciences, Delft University of Technology, 2628 CN Delft, The Netherlands
Beyond Numerical Time Series: A Unified Benchmark for Multimodal Forecasting with Heterogeneous Context
Sep 14, 2026Peng Chen, Zhihao Zhuang, Hongzhou Chen +6Time Series Foundation Models Multimodal Benchmarks
Ant International · Hangzhou, China
Evaluating Time-Series Foundation Models and Multimodal Dietary Context for CGM Forecasting
Sep 11, 2026Bowen Zhang, Hsiu-Wen Cheng, Hongyu Yang +9Continuous Glucose Monitoring Diabetes
University of California, Los Angeles, Los Angeles, CA, U.S.A. · Union County Magnet High School, Scotch Plain, NJ, U.S.A. · Huntington Beach High School, Huntington Beach, CA, U.S.A. +3
Distillation of Synthetic Data for Time Series Foundation Models
Sep 9, 2026Niloy Biswas, Noureddine El KarouiTime Series Foundation Models Diffusion-Based Dataset Distillation
Meta AI
When the Martingale Never Stops Firing: Anytime-Valid Gating on Real Forecast Streams
Aug 31, 2026Weijia Han, Lisha QuConformal Test Martingales Non-Stationarity
University of Washington · Seattle, USA
ChorusTIC: Training-Free Multivariate Time Series Classification via Chorus In-Context Learning
Aug 25, 2026Juntao Fang, Shifeng Xie, Ruichu Cai +6Time-Series Classification Time Series Foundation Models
Guangdong University of Technology · Huawei Noah’s Ark Lab · Université Paris Cité +2
Into the ORBIT for Time Series: Training Regimes for Foundation Models
Aug 13, 2026Hongjie Xia, Yiding Liu, Yifan Hu +2Time Series Foundation Models Zero-Shot Forecasting
Long-Horizon Forecasting of Complete Financial Statements with Forma
Aug 11, 2026Travis L. Johnson, Jiannan Jiang, Soumyabrata Chaudhuri +3Data-Driven Forecasting Long-Horizon Forecasting
University of Texas at Austin, Austin, TX, USA
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 MondalTime Series Foundation Models Cnn-Lstm
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 Foundation Models Reinforcement Learning Post-Training
Institute of Software, Chinese Academy of Sciences, Beijing, China
Beyond Foundation Models: Dimension-Aware Neural Architecture Search with Small-Data Representation Models for Cryocooler Lifetime Prediction
Aug 7, 2026Gregor Molan, Grafika Jati, Francesco Barchi +3Time Series Foundation Models Representation Learning
Comtrade 360 d.o.o., Letališka cesta 29b, Ljubljana, 1000, Slovenia · Comtrade AI GmbH, Grafenauweg 8, Zug, 6300, Switzerland · Alma Mater Studiorum – Università di Bologna, Department of Electrical, Electronic, and Information Engineering (DEI), Viale del Risorgimento 2, Bologna, 40136, Italy +2
Do Tabular Foundation Models Agree with Themselves?
Aug 6, 2026Christian Klötergens, Vijaya Krishna Yalavarthi, Lars Schmidt-Thieme +1Tabular Foundation Models Time Series Foundation Models
Institute of Computer Science & VWFS DARC, University of Hildesheim, Hildesheim, Germany
Align-RAG: Alignment Is All You Need for TSFM In-Context Learning
Aug 6, 2026Mohammad Asadi, Soheil Hor, Bardiya Akhbari +6Retrieval-Augmented Forecasting Time Series Foundation Models
1Stanford University 2Amazon
Personalized Federated Sparse Adaptation of Time-Series Foundation Models
Aug 5, 2026Priyanka Nihalchandani, Naman Srivastava, Varun Ojha +1Time Series Foundation Models Parameter-Efficient Adaptation
Robert Bosch Centre for Cyber-Physical Systems, Indian Institute of Science, Bengaluru, India · School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
FinVerse: Financial Time-Series Benchmark
Aug 4, 2026Jaehoon Lee, Jun Seo, Seunghan Lee +9Finance Benchmarks Time Series Foundation Models
LG AI Research
FedChronos: Federated Fine-Tuning of Time-Series Foundation Models for Privacy-Preserving Commodity Price Forecasting
Aug 2, 2026Amit Sharma, Nitin Auluck, Akramul AzimTime Series Foundation Models Wireless Foundation Models
Indian Institute of Technology Ropar, Punjab, India · Ontario Tech University, Oshawa, Canada
Revisiting Predictive Process Monitoring in the Age of Foundation Models: A Comparative Study of Sequence, Tabular, and LLM Approaches
Jul 30, 2026Lennart Fertig, Lukas Kirchdorfer, Tobias SesterhennTime Series Foundation Models Sequence Modeling
University of Mannheim, Mannheim, Germany · SAP Signavio, Walldorf, Germany · Technical University of Clausthal, Clausthal, Germany
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 Foundation Models Large Language Model Planning
Applied Artificial Intelligence Initiative Deakin University Geelong, Australia
Zero-Shot Heart Rate Variability Forecasting from Consumer Wearables Using Time Series Foundation Models
Jul 22, 2026Luukas Peräkylä, Fahad Sohrab, Ville Hautamäki +3Time Series Foundation Models Heart Rate
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 +7Time Series Foundation Models Pretraining
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 +2Time Series Foundation Models Data-Driven 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 Forecasting Pedestrian Trajectory Prediction
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 +4Time Series Foundation Models Zero-Shot 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 Foundation Models Foundation Model
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 +3Accurate Short-Term Electricity Load Forecasting Forecasting Backbone
Karlsruhe Institute of Technology, Germany · Helmholtz AI, Germany
Tabular Foundation Models for Discrete Choice Estimation
Jul 14, 2026Liu Liu, Dan ZhangDiscrete Choice Models Tabular Foundation Models
Leeds School of Business, University of Colorado Boulder
Exploring Zero-Shot Foundation Models for Multivariate Time Series Anomaly Detection
Jul 14, 2026Martin Uray, Saverio Messineo, Roland Kwitt +1Time-Series Anomaly Detection Time Series Foundation Models
Josef Ressel Centre for Intelligent and Secure Industrial Automation, Salzburg University of Applied Sciences, Austria · Department of Artificial Intelligence and Human Interfaces, Paris Lodron University of Salzburg, Austria
A Foundation Model for Multimodal Event Sequences in Financial Applications
Jul 10, 2026Nikita Rusakov, Vladislav Meshkov, Konstantin Zorin +4Multimodal Foundation Model Time Series Foundation Models
Sber Moscow, Russian Federation · Sber AI Lab Moscow, Russian Federation
Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5
Jul 8, 2026Yongcan Huang, Li Jiang, Ze Yu LiuWildfire Prediction Time Series Foundation Models
College of Engineering, University of Georgia, Athens, Georgia, USA · Information System Department, UMBC, Baltimore, Maryland, USA · College of Graduate and Professional Studies, Trine University, Angola, Indiana, USA +1
RMISC: A Large-scale Real-world Multivariate Corpus for Time Series Foundation Models
Jul 7, 2026Qian Sun, Yong-Ming Tian, Jia-Wei Huang +2Time Series Foundation Models Timestep Embeddings
State Key Laboratory of Novel Software Technology, Nanjing University, Nanjing, China · School of Intelligent Science and Technology, Nanjing University, Suzhou, China · Nanjing University – Siemens Joint Research Center on Industrial AI, Suzhou, China +1

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Attenuated in-context identification in time-series foundation models: diagnosis under counterfactual inputs and repair by synthetic forced-system fine-tuning

Hong-In Won

Manufacturing AI Research Center, Korea Institute of Industrial Technology, Incheon, Republic of Korea · HYU-KITECH Joint Department, Graduate School, Hanyang University, Seoul, Republic of Korea

Time Series Foundation Models

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Covariate-aware time-series foundation models (TSFMs) promise training-free what-if answers for instrumented plants: the change in output that a different future input would cause. We test this on forced engineering systems with exact counterfactuals, comparing Chronos-2, TimesFM-2.5 and TabPFN-TS with classical system identification fitted to the same context. Through their default covariate interfaces, TimesFM-2.5 and TabPFN-TS are memoryless: the predicted effect of an input change is a same-time function of that change (

R^2 = 1.000

for TimesFM-2.5). Chronos-2 identifies dynamics in context but attenuates them. Its predicted effect is 0.33-0.80 of the true effect, its recovered impulse response has the wrong shape, and its error on a one-degree-of-freedom oscillator levels off at 0.57 with 8192 context samples, where ARX fitted to 256 samples reaches 0.02. Context dither at inference lowers the what-if error on all six synthetic classes without training. A 26-minute fine-tune on synthetic forced systems restores the response magnitude (sensitivity 0.83-0.96) and outperforms structure-agnostic identification on Wiener-Hammerstein and a held-out friction class. A specialised in-context identifier trained on the same data comes close, so the forced-system data carry most of the gain. On three of four measured plants classical identification remains clearly better, and the fine-tuned model loses part of its univariate forecasting skill. Paired counterfactual inputs, together with shuffled future inputs on measured records, test two properties: whether the covariate interface can represent dynamics and whether the pretraining prior covers the plant's time scale. Only the counterfactual pairs expose the attenuation.

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