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.

STRIDE: Reasoning-Aware Training for Time Series Forecasting
May 9, 2026Md Atik Ahamed, Mihir Parmar, Palash Goyal +4Time Series Reasoning Time Series Forecasting
Google · University of Kentucky
Multivariate Financial Forecasting using the Chronos Time Series Foundation Models
May 8, 2026Sanjiv R Das, Tarang Goyal, Mohini YadavMultivariate Time Series Forecasting Quantitative Finance
Santa Clara University
Emergent Symbolic Structure in Health Foundation Models: Extraction, Alignment, and Cross-Modal Transfer
May 8, 2026Gajendra Katuwal, Advait Koparkar, Salar Abbaspourazad +2Cross-Modal Learning Representation Learning
Apple
PIMSM: Physics-Informed Multi-Scale Mamba for Stable Neural Representations under Distribution Shift
May 8, 2026Sangyoon Bae, Shinjae Yoo, Jiook ChaDomain Generalization Physics-Informed ML
Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul, South Korea, 08826 · Department of Psychology Seoul National University Seoul, South Korea, 08826
Preliminary Insights in Chronos Frequency Data Understanding and Reconstruction
May 7, 2026Alessandro Pagani, Marco Cominelli, Liying Han +11Frequency-Domain Feature Learning Time Series Representation Learning
DII, University of Brescia, Italy. · DEIB, Politecnico di Milano, Italy. · ECE Department, University of California, Los Angeles, USA. +6
Retrieval Mechanisms Surpass Long-Context Scaling in Time Series Forecasting
May 6, 2026Rishi Ahuja, Kumar Prateek, Simranjit Singh +1Time Series Forecasting Long-Context Modeling
Department of Information Technology, Dr. B.R. Ambedkar National Institute of Technology Jalandhar, Punjab, 144008, India.
Explainable Load Forecasting with Covariate-Informed Time Series Foundation Models
Apr 30, 2026Matthias Hertel, Alexandra Nikoltchovska, Sebastian Pütz +3Explainable Time Series Forecasting Time Series Forecasting
Karlsruhe Institute of Technology, Karlsruhe, Germany
Domain-Adapted Fine-Tuning of ECG Foundation Models for Multi-Label Structural Heart Disease Screening
Apr 25, 2026Duc N. Do, Minh N. Do, Dang Nguyen +14Unsupervised Domain Adaptation Echocardiography
1PASSIO Laboratory, North Carolina A&T State University, Greensboro, NC, USA · Department of Computer Science, University of Manitoba, Winnipeg, MB, Canada · 3Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA +4
FETS Benchmark: Foundation Models Outperform Dataset-specific Machine Learning in Energy Time Series Forecasting
Apr 24, 2026Marco Obermeier, Marco Pruckner, Florian Haselbeck +1Time Series Forecasting Forecasting Benchmarks
Julius-Maximilians-Universität Würzburg, Modeling and Simulation Lab, Am Hubland, Würzburg, 97074, Germany · Weihenstephan-Triesdorf University of Applied Sciences, Smart Farming, Am Staudengarten 1, Freising, 85354, Germany · Weihenstephan-Triesdorf University of Applied Sciences, Digital Energy Transition, Hans-Carl-von-Carlowitz-Platz 3, Freising, 85354, Germany
Calibrating Scientific Foundation Models with Inference-Time Stochastic Attention
Apr 21, 2026Akash Yadav, Taiwo A. Adebiyi, Ruda ZhangPost-Hoc Calibration Probabilistic Forecasting
University of Houston
In-Context Learning Under Regime Change
Apr 18, 2026Carson Dudley, Yutong Bi, Xiaofeng Liu +1Transformer Distribution Shift Detection
Department of Mathematics, University of Michigan · Department of Electrical Engineering and Computer Science, University of Michigan · Michigan Institute for AI and Data in Society, University of Michigan
TimeRFT: Stimulating Generalizable Time Series Forecasting for TSFMs via Reinforcement Finetuning
Apr 18, 2026Siyang Li, Yize Chen, Zijie Zhu +4Time Series Forecasting OOD Generalization
HKUST(GZ) Guangzhou, China · University of Alberta Edmonton, Canada · Alibaba Cloud Guangzhou, China +3
Assessing the Potential of Masked Autoencoder Foundation Models in Predicting Downhole Metrics from Surface Drilling Data
Apr 16, 2026Aleksander Berezowski, Hassan Hassanzadeh, Gouri GindeMultivariate Time Series Forecasting Masked Autoencoders
Schulich School of Engineering Department of Electrical and Software Engineering University of Calgary Calgary, Canada · Schulich School of Engineering Department of Chemical and Petroleum Engineering University of Calgary Calgary, Canada
Predicting Power-System Dynamic Trajectories with Foundation Models
Apr 16, 2026Haoran Li, Lihao Mai, Chenhan Xiao +2Neural Surrogate Modeling Zero-Shot Time Series Forecasting
School of Electrical, Computer and Energy Engineering at Arizona State University, Tempe, AZ 85281 USA · MOVEJ Analytics, Dayton, OH 45324 USA
Assessing the Performance-Efficiency Trade-off of Foundation Models in Probabilistic Electricity Price Forecasting
Apr 16, 2026Jan Niklas Lettner, Hadeer El Ashhab, Veit Hagenmeyer +1Electricity Price Forecasting Time Series Forecasting
Karlsruhe Institute of Technology · Karlsruhe, Germany
Brain-DiT: A Universal Multi-state fMRI Foundation Model with Metadata-Conditioned Pretraining
Apr 14, 2026Junfeng Xia, Wenhao Ye, Xuanye Pan +3Medical Imaging Foundation Models Diffusion Transformer
Department of Biomedical Engineering, Southern University of Science and Technology, China · School of Biomedical Engineering, Shenzhen University, China
TempusBench: An Evaluation Framework for Time-Series Forecasting
Apr 13, 2026Denizalp Goktas, Gerardo Riaño-Briceño, Omkar Tekawade +14Time Series Forecasting Forecasting Benchmarks
Simulacrum New York City, NY, USA
WaveMoE: A Wavelet-Enhanced Mixture-of-Experts Foundation Model for Time Series Forecasting
Apr 12, 2026Shunyu Wu, Jiawei Huang, Weibin Feng +6Time Series Forecasting Time-Series Foundation Models
Sun Yat-sen University · Xiaomi Corporation · National University of Singapore
ADAPTive Input Training for Many-to-One Pre-Training on Time-Series Classification
Apr 9, 2026Paul Quinlan, Qingguo Li, Xiaodan ZhuTime Series Representation Learning Time-Series Foundation Models
Electrical and Computer Engineering, Queen’s University · Ingenuity Labs Research Institute, Queen’s University · Mechanical and Materials Engineering, Queen’s University
Bi-level Heterogeneous Learning for Time Series Foundation Models: A Federated Learning Approach
Apr 8, 2026Shengchao Chen, Guodong Long, Dikai Liu +1Time-Series Foundation Models Time Series Forecasting
Australian AI Institute, University of Technology Sydney · Robotics Institute, University of Technology Sydney
Thermal-GEMs: Generalized Models for Building Thermal Dynamics
Apr 7, 2026Felix Koch, Fabian Raisch, Benjamin TischlerTime Series Forecasting Multi-Source Domain Adaptation
Technical University of Applied Sciences Rosenheim, Rosenheim, Germany · Technical University of Munich, Munich, Germany
Discrete Prototypical Memories for Federated Time Series Foundation Models
Apr 6, 2026Liwei Deng, Qingxiang Liu, Xinhe Niu +5Time-Series Foundation Models Federated Learning
Australian Artificial Intelligence Institute, University of Technology Sydney · The Hong Kong University of Science and Technology (Guangzhou) · Shenzhen University +2
Non-Stationarity in the Embedding Space of Time Series Foundation Models
Apr 6, 2026Jinmyeong Choi, Brad Shook, Artur DubrawskiTime-Series Foundation Models Non-Stationary Time Series
Auton Lab Robotics Institute Carnegie Mellon University Pittsburgh, PA 15213, USA
A Family of Open Time-Series Foundation Models for the Radio Access Network
Apr 5, 2026Ioannis Panitsas, Leandros TassiulasWireless Foundation Models Time-Series Foundation Models
Department of Electrical and Computer Engineering, Yale University
CalM: A Self-Supervised Foundation Model for Population Dynamics in Calcium Imaging Data
Apr 3, 2026Xinhong Xu, Yimeng Zhang, Qichen Qian +1Self-Supervised Pre-Training Time-Series Foundation Models
Department of Automation, Tsinghua University, Beijing, China · School of Life Sciences, Tsinghua University, Beijing, China
QuitoBench: A High-Quality Open Time Series Forecasting Benchmark
Mar 27, 2026Siqiao Xue, Zhaoyang Zhu, Wei Zhang +7Time Series Forecasting Time Series Modeling
Ant Group
A Foundation Model for Instruction-Conditioned In-Context Time Series Tasks
Mar 23, 2026Anish Saha, Konstantin ShmakovZero-Shot Time Series Forecasting Meta-Learning
Walmart Sunnyvale, USA
CoRA: Boosting Time Series Foundation Models for Multivariate Forecasting through Correlation-aware Adapter
Mar 23, 2026Hanyin Cheng, Xingjian Wu, Yang Shu +4Multivariate Time Series Forecasting Time-Series Foundation Models
East China Normal University · Huawei Noah’s Ark Lab
STEP: Scientific Time-Series Encoder Pretraining via Cross-Domain Distillation
Mar 19, 2026Chen Zhang, Liwei Liu, Jun Tao +6Time Series Representation Learning Time-Series Foundation Models
Shanghai Aritificial Intelligence Laboratory · Tsinghua University · Harbin Institute of Technology +2
msData: A Millisecond-Resolution Network Dataset for Advancing Time Series Foundation Models
Mar 17, 2026Subina Khanal, Seshu Tirupathi, Merim Dzaferagic +2Wireless Local Area Networks Time-Series Foundation Models
Department of Computer Science Aalborg University · IBM Research, Ireland · Trinity College Dublin

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STRIDE: Reasoning-Aware Training for Time Series Forecasting

Md Atik Ahamed, Mihir Parmar, Palash Goyal, Chun-Liang Li+3

Google · University of Kentucky

Time Series Reasoning Time Series Forecasting LLM-Based Time Series Forecasting Time-Series Foundation Models Probabilistic Time Series Forecasting

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Time Series Foundation Models (TSFMs) excel at numerical forecasting but operate as black boxes lacking qualitative reasoning. Conversely, applying LLMs directly to temporal data introduces a modality gap: text tokenizers fragment continuous numerical values, degrading mathematical relationships and exploding sequence lengths, leading to computational overhead. To resolve this, we introduce STRIDE (Strategic Time-series Reasoning Injected via Distilled Embeddings), a novel framework natively integrating LLM reasoning into the continuous embedding space of TSFMs. Instead of discrete tokens, STRIDE distills reasoning traces into a lightweight LLM and dynamically projects its pooled hidden states as a continuous cross-modal prior into the numerical encoder. Jointly optimized via cross-entropy and quantile losses, STRIDE readily attaches to diverse architectures (e.g., Chronos-2, Timer-S1). STRIDE ranks #1 on the GIFT-Eval leaderboard (0.625 MASE, 0.423 CRPS) when ensembled with Synapse. On TFRBench, STRIDE consistently outperforms baselines in both forecasting accuracy and reasoning quality, with ablations confirming gains stem from strategic reasoning. Thus, injecting semantic reasoning as a continuous prior equips TSFMs with human-interpretable reasoning while improving predictive accuracy.