Time-Series Foundation Models

Follow this topic Ask about this topic

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.

Latest papers

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

A Hybrid Attention Model Learning Unified Time-aware Patch Representation for Irregular Multivariate Time Series Forecasting
Sep 19, 2026Li Lin, Zhihao Lin, Qi Zhang +3Irregular Time-Series Modeling Multivariate Time Series Forecasting
Southeast University, China · Nanyang Technological University, Singapore · Timecho Ltd., China
Tracing the Evidence Behind Zero-Shot Time-Series Forecasting: A Source-First Taxonomy and Audit Framework
Sep 18, 2026Delun Kong, Wanyun Ling, Chenxi Liu +1Zero-Shot Time Series Forecasting LLM-Based Time Series Forecasting
Department of Operations and Technology Technical University of Munich Heilbronn, Germany · Hong Kong Institute of Science & Innovation Chinese Academy of Sciences Hong Kong, China
Knowledge-Graph-Augmented Chronos-2 for HEC-RAS Surrogate Forecasting
Sep 18, 2026Edward Holmberg, Elias Ioup, Mahdi AbdelguerfiSurrogate Modeling Flood Forecasting
Canizaro-Livingston Gulf States Center for Environmental Informatics New Orleans, LA, USA · Center for Geospatial Sciences Naval Research Laboratory Stennis Space Center, Mississippi, USA
FreqCondNorm: Towards Cross-domain Predictive Maintenance through a Frequency-Conditioned Transformer Foundation Model
Sep 17, 2026Zaynab Raounak, Camille LHermine, Zhiguo ZengMulti-Source Domain Adaptation Self-Supervised Time Series Representation Learning
Laboratoire Génie Industriel, CentraleSupélec, Université Paris-Saclay, France
QUALS: Corpus Equilibrium for Universal Forecasting via Pattern Quantization and Learnability Synchronization
Sep 17, 2026Yujie Li, Zezhi Shao, Chengqing Yu +7Zero-Shot Time Series Forecasting Data Selection
State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China · University of Chinese Academy of Sciences, Beijing, China · School of Data Science and Engineering, East China Normal University, Shanghai, China
WaveTLM: Reliable Time-Series Language Modeling through Task Compilation
Sep 16, 2026Jiahui Chen, Bingke Zhu, Hongyu Pan +1LLM Reliability Time-Series Foundation Models
Institute of Automation, Chinese Academy of Sciences Beijing, China
TW3Cast: A Frozen Router of Lightly Fine-Tuned Foundation Models for Time-Series Forecasting on GIFT-Eval, Selected Entirely on the Training Split
Sep 16, 2026Nathan Thierry, Andre-Louis RochetTime Series Forecasting Time-Series Foundation Models
TW3 Partners
Peak-Aware Short-Term Load Forecasting Across Distribution Grid Aggregation Levels
Sep 16, 2026Souhardya Chattopadhyay, Julian Oelhaf, Antonia Schoening +5Time Series Forecasting Electricity Load Forecasting
Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg · Siemens AG, Smart Infrastructure · Department of Electrical Engineering, Media and Computer Science, Ostbayerische Technische Hochschule Amberg-Weiden
SPEAR NeXT Causal Latent Forecasting Across Multiple Horizons for Spectral Temporal Earth Representation Learning
Sep 15, 2026Rajiv Ranjan, Udaiveer Singh, Shashank Tamaskar +1Multivariate Time Series Forecasting Geospatial Foundation Models
Plaksha University Mohali, Punjab, India · Purdue University West Lafayette, Indiana, USA
SOTER: A Generative Time-Series Foundation Model for Wearable Human Physiological Signals
Sep 15, 2026Fangke Chen, Sirry Chen, Wei Chen +1Irregular Time-Series Modeling Time Series Imputation
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 +2Crowd Counting Time Series Forecasting
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 +6Multivariate Time Series Forecasting Time Series Forecasting
Ant International · Hangzhou, China
Tabby: An Open Pretraining Recipe for Time Series Foundation Models
Sep 12, 2026Shifeng Xie, Bahaeddine Abdessalem, Zehao Xiao +9Time-Series Foundation Models Time Series Transformers
Huawei Noah’s Ark Lab, Paris, France · LIPADE, Université Paris Cité, Paris, France · École Polytechnique, France +3
FlowTSFM: Turning Encoder Depth into Quantile Transport
Sep 12, 2026Bahaeddine Abdessalem, Shifeng Xie, Zehao Xiao +6Time-Series Foundation Models Probabilistic Time Series Forecasting
Huawei Noah’s Ark Lab, Paris, France · École Polytechnique, France · LIPADE, Université Paris Cité, Paris, France +3
Evaluating Time-Series Foundation Models and Multimodal Dietary Context for CGM Forecasting
Sep 10, 2026Bowen Zhang, Hsiu-Wen Cheng, Hongyu Yang +9Zero-Shot Time Series Forecasting Blood Glucose Forecasting
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
A Later Test Set Is Not a New Domain: Pretraining Familiarity Survives a Contamination-Free Hold-Out
Sep 9, 2026Mahdi Naser Moghadasi, Faezeh GhaderiZero-Shot Time Series Forecasting Domain Generalization
BrightMind AI · University of Texas at Arlington
Distillation of Synthetic Data for Time Series Foundation Models
Sep 9, 2026Niloy Biswas, Noureddine El KarouiSynthetic Data Pretraining Time-Series Foundation Models
Meta AI
IPM-FM: A Foundation Model with Consensus Feature Selection for Industrial Process Monitoring
Sep 8, 2026Liang Cao, Weide Liu, Yan Qin +3Predictive Process Monitoring Process Supervision
Department of Chemical and Biological Engineering University of British Columbia Vancouver, BC, Canada · School of Computing and Artificial Intelligence Jiangxi University of Finance and Economics Nanchang, China · School of Automation Chongqing University Chongqing, China +2
EXAONE Forecast for Finance
Sep 7, 2026Seunghan Lee, Jaehoon Lee, Jun Seo +9Multivariate Time Series Forecasting Financial Forecasting
LG AI Research∗
Assessing Covariate-Informed Grid Load Forecasting with a Time-Series Foundation Model
Sep 6, 2026Varsha Pendyala, Yiwei Fu, Weizhong Yan +1Exogenous Time Series Forecasting Electricity Load Forecasting
Artificial Intelligence Team GE Vernova Advanced Research Center, Niskayuna, NY, USA
Cadence: Error-Bounded Lossy Compression of Demand Time Series with a Time-Series Foundation Model
Sep 5, 2026Roberto TacconelliPredictive Coding Time Series Modeling
Independent Researcher
OutageDiT: A Foundation Model for Power Outage Forecasting and Scenario Generation
Sep 1, 2026Yunqin Zhu, Feng Qiu, Yao XieConditional Generative Modeling Time-Series Foundation Models
H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology · Argonne National Laboratory
TSPFN: A Temporal Tabular Foundation Model for Physiological Time Series Classification
Aug 31, 2026Jérémie Stym-Popper, Clément Rambour, Federica Granese +2Tabular Foundation Models Time Series Classification
Sorbonne Université, CNRS, ISIR, MLIA, F-75005 Paris, France · Inria, CNRS, I3S, Université Côte d’Azur, Valbonne, France · INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1294, F-69621, LYON, France +1
Foundation models for electricity price forecasting and battery arbitrage: Can they replace market-specific forecasting models?
Aug 31, 2026Arkadiusz Lipiecki, Rafał WeronTabular Foundation Models Electricity Price Forecasting
Department of Computational Social Science, Wrocław University of Science and Technology, Poland · Department of Operations Research and Business Intelligence, Wrocław University of Science and Technology, Poland · Center for Research in Energy (CoRE), Aarhus University, 8000 Aarhus C, Denmark
EncoTESS: Age-Sensitive Encodings from Raw TESS Light Curves
Aug 25, 2026Phil R. Van-Lane, Joshua S. Speagle, Ryan Cloutier +3Astrophysics Time Series Representation Learning
David A. Dunlap Department of Astronomy and Astrophysics, University of Toronto · Dunlap Institute for Astronomy and Astrophysics, University of Toronto · Department of Astronomy and Astrophysics, University of California San Diego +11
Causal Analysis for Time Series Foundation Models
Aug 25, 2026Mathis Jander, Wouter van Heeswijk, Martijn MesTime-Series Foundation Models Time Series Modeling
Faculty of Behavioral, Management and Social Sciences, University of Twente, Enschede, Netherlands · European Central Bank, Frankfurt am Main, Germany
ChorusTIC: Training-Free Multivariate Time Series Classification via Chorus In-Context Learning
Aug 25, 2026Juntao Fang, Shifeng Xie, Ruichu Cai +6Multivariate Time Series Classification Time Series Classification
Guangdong University of Technology · Huawei Noah’s Ark Lab · Université Paris Cité +2
MnemoDyn: Learning Resting State Dynamics from 40K FMRI sequences
Aug 25, 2026Sourav Pal, Viet Luong, Hoseok Lee +5fMRI Foundation Models Neuroimaging
University of Wisconsin–Madison, Madison, United States · Pohang University of Science and Technology (POSTECH), Pohang, South Korea · University of North Carolina at Chapel Hill, Chapel Hill, United States
Do Time-Series Foundation Models Pay Off for Industrial Monitoring? A Cost-Aware Empirical Study
Aug 24, 2026Guan-Hua Wen, Kuan-Yu ChenTime-Series Foundation Models Time-Series Anomaly Detection
Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei, Taiwan
Systematic Evaluation of TabPFN-TS and Chronos-2 for Zero-Shot Heat Load Forecasting in District Heating Networks
Aug 20, 2026Ben Spoek, Karim K. Ben Hicham, Kai Derzsi +3Zero-Shot Time Series Forecasting Time Series Forecasting
Chair of Energy Efficient Buildings and Indoor Climate, RWTH Aachen University, Mathieustr. 10, 52074 Aachen, Germany · Process Systems Engineering, RWTH Aachen University, Forckenbeckstr. 51, 52074 Aachen, Germany

Sep 19, 2026J/K move · Enter open · S save

A Hybrid Attention Model Learning Unified Time-aware Patch Representation for Irregular Multivariate Time Series Forecasting

Li Lin, Zhihao Lin, Qi Zhang, Kaiwen Xia+2

Southeast University, China · Nanyang Technological University, Singapore · Timecho Ltd., China

Irregular Time-Series Modeling Multivariate Time Series Forecasting Time Series Forecasting Hybrid Attention Time-Series Foundation Models

Open paper AskPDF arXiv

Time series foundation models (TSFMs) have recently delivered impressive zero-shot performance across diverse forecasting tasks. However, real-world decision-making frequently relies on irregular multivariate time series (IMTS), where inconsistent inter-observation intervals and asynchronous sampling across variables coexist with informative missingness. Existing TSFMs handle such inputs either through imputation that injects spurious values or through index-based positional encodings that ignore continuous time. There is still a gap in the foundation model that follows the original IMTS patterns. In this paper, we propose a hybrid attention model that learns a unified time-aware patch representation for IMTS forecasting. We first design a time-aware patch encoding that maps a variable number of intra-patch timestamps into a fixed-size embedding, producing a uniform format for irregular patches without resorting to imputation. We then introduce a time bias attention mechanism that calibrates inter-patch temporal misalignment and asynchronous cross-channel dependencies as auxiliary attention offset. Finally, on top of a decoder-only Transformer backbone, we adopt a hybrid causal mask that preserves a bidirectional full view over the historical context while keeping the forecast horizon strictly autoregressive. To support large-scale pretraining under irregular settings, we also curate VersaTSA, an archive of

30

B observations that retains the native sampling sparsity of its sources. Experiments on three IMTS benchmarks and a standard regular-MTS benchmark show that our model achieves state-of-the-art zero-shot performance on IMTS and remains competitive when transferred to regular forecasting.