Time Series Modeling

Latest papers

51 papers14 papers in the last four weeks, up 56% on the four weeks before. 0.1% of all new papers.

When Do Foundation Models Pay Off? A Break-Even Analysis of Pretrained Time Series Forecasters
Jul 6, 2026Nicholas Tan Jerome, Frank SimonZero-Shot Time Series Forecasting Time Series Forecasting
Karlsruhe Institute of Technology (KIT) Eggenstein-Leopoldshafen, Germany
MACROCAST: A Vintage-Consistent Time Series Foundation Model for Real-Time Macroeconomic Forecasting
Jun 27, 2026Andrea Carriero, Davide Pettenuzzo, Shubhranshu ShekharData Leakage Time Series Forecasting
School of Economics and Finance, Queen Mary University of London. · School of Business and Economics, Brandeis University.
Global Explanations for Multivariate Time Series Forecasting Models via $K$ -Order Markov Approximations
Jun 25, 2026Amadeo TunyiMarkov Models Surrogate Modeling
XITASO GmbH Austraße 35, 86153 Augsburg, Germany
How Good Can Linear Models Be for Time-Series Forecasting?
Jun 25, 2026Lang Huang, Jinglue Xu, Luke DarlowTime Series Forecasting Hyperparameter Optimization
1Sakana AI, Tokyo, Japan · 2National Institute of Informatics, Japan
Quantizing Time-Series Models As Dynamical Systems: Trajectory-Based Quantization Sensitivity Score
Jun 11, 2026Mariya Pavlova, Harrison Bo Hua Zhu, Lidia Vitanova +2Mixed-Precision Quantization Time Series Modeling
Department of Computing, Imperial College London, London, United Kingdom · Department of Public Health, University of Copenhagen, Copenhagen, Denmark · GATE Institute, Sofia University St. Kliment Ohridski, Sofia, Bulgaria +1
Stationarity-Aware Retrieval-Augmented Time Series Forecasting
Jun 2, 2026Shiqiao Zhou, Holger Schöner, Zipeng Wu +3Time Series Forecasting Non-Stationary Time Series Forecasting
University of Birmingham Birmingham, United Kingdom · Siemens AG Munich, Germany
Time Series as Language: A Universal Tokenizer for General-Purpose Time Series Foundation Models
May 31, 2026Yunhao Zhang, Ruiying Qi, Jiale Zheng +3Time Series Tokenization Time Series Generation
Shanghai Jiao Tong University · Huawei Noah’s Ark Lab
VESTA: Visual Exploration with Statistical Tool Agents
May 29, 2026William Rudman, Abhishek Divekar, Kanishk Jain +6Time Series Modeling Tool-Using Vision-Language Agents
The University of Texas at Austin · New York University
Generalizing Multi-Scale Time-Series Modeling with a Single Operator
May 29, 2026Cheonwoo Lee, Dooho Lee, Doyun Choi +1Long-Term Time Series Forecasting Time Series Forecasting
School of Electrical Engineering, KAIST, Daejeon, Republic of Korea · Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea
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
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
TopoPrimer: The Missing Topological Context in Forecasting Models
May 14, 2026Zara Zetlin, Kayhan Moharreri, Maria SafiTime Series Forecasting Sheaf Theory
Apple
CombinationTS: A Modular Framework for Understanding Time-Series Forecasting Models
May 2, 2026Xiaorui Wang, Fanda Fan, Chenxi Wang +9Time Series Forecasting Forecasting Benchmarks
Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China · University of Chinese Academy of Sciences, Beijing, China · Beijing Normal University - Hong Kong Baptist University United International College, Zhuhai, China +1
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
TimeSeriesExamAgent: Creating Time Series Reasoning Benchmarks at Scale
Apr 11, 2026Malgorzata Gwiazda, Yifu Cai, Mononito Goswami +2Time Series Reasoning LLM Evaluation
Technical University of Munich, Munich, Germany · Auton Lab, School of Computer Science, Carnegie Mellon University, Pittsburgh, US
MR-ImagenTime: Multi-Resolution Time Series Generation through Dual Image Representations
Mar 30, 2026Xianyong Xu, Yuanjun Zuo, Zhihong Huang +4Time Series Modeling Time Series Forecasting
Department, State Grid Hunan Electric Power Company Limited Research Institute & Hunan Province Engineering Technology Research Center of Electric Power Multimodal Perception and Edge Intelligence, Changsha, Hunan, China. · College of Computer Science, Hunan University, Changsha, Hunan, 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
Seeking SOTA: Time-Series Forecasting Must Adopt Taxonomy-Specific Evaluation to Dispel Illusory Gains
Mar 16, 2026Raeid Saqur, Christoph Bergmeir, Blanka Horvath +3Time Series Forecasting Time Series Modeling
Dept. of Computer Science, University of Toronto · Dept. of Mathematics, University of Oxford · Vector Institute +5
Position: A Dynamical Systems Perspective is Needed to Advance Time Series Modeling
Feb 18, 2026Daniel Durstewitz, Christoph Jürgen Hemmer, Florian Hess +2Time Series Modeling Time Series Forecasting
Dept. of Theoretical Neuroscience, Central Institute of Mental Health, Mannheim, Germany · Faculty of Physics & Astronomy, Heidelberg Univ., Heidelberg, Germany · Interdisciplinary Center for Scientific Computing, Heidelberg, Germany
Variability Aware Recursive Neural Network (VARNN): A Residual-Memory Model for Capturing Temporal Deviation in Sequence Regression Modeling
Oct 10, 2025Haroon Gharwi, Yue Dai, Kai ShuMemory-Augmented Neural Networks Residual Learning
Department of Computer Science, Illinois Institute of Technology, Chicago, IL, USA · Department of Computer Science, Emory University, Atlanta, GA, USA
Interpretable time series analysis with Gumbel dynamics
Sep 25, 2025Yiliu Wang, Timothy Doyeon Kim, Eric Shea-Brown +1Time Series Modeling Latent Dynamics Modeling
Allen Institute · University of Washington

Jul 6, 2026J/K move · Enter open · S save

When Do Foundation Models Pay Off? A Break-Even Analysis of Pretrained Time Series Forecasters

Nicholas Tan Jerome, Frank Simon

Karlsruhe Institute of Technology (KIT) Eggenstein-Leopoldshafen, Germany

Zero-Shot Time Series Forecasting Time Series Forecasting Forecasting Benchmarks Time-Series Foundation Models Time Series Modeling

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Deploying a time series foundation model requires GPU infrastructure, engineering overhead, and carries no guarantee of improvement over XGBoost. We provide the first systematic break-even analysis answering when this investment pays off. Across 30 benchmark datasets, we compare zero-shot and LoRA fine-tuned foundation models (Chronos, Moirai, Lag-Llama) against classical baselines (Naive, ETS, ARIMA, XGBoost) at six training set sizes from 2% to 100% of available data. Foundation models outperform classical methods at every evaluated training fraction on 15 of 30 datasets -- GPU deployment is unconditionally justified on these regardless of data volume. On 6 datasets, classical methods surpass zero-shot foundation models with as little as 2% of training data (21-2,768 samples); on the remaining 9, break-even ranges from 24 to 8,361 samples. One robust deployment rule requires no model training: if n_train < 700 and seasonality is non-negligible, use FM zero-shot and skip fine-tuning -- this resolves 10 of 30 deployment decisions immediately. Contrary to common practice, LoRA fine-tuning can actively degrade performance on short series. We operationalise these findings as a two-step decision framework -- compute dataset length and seasonality strength, run a brief 5-10% pilot only if needed -- enabling practitioners to make the FM-versus-classical decision before committing to full infrastructure. Four dataset features motivate mechanistic hypotheses for the remaining cases, though reliable automated prediction at this benchmark scale remains an open problem. Code, benchmark, and decision tools are available at https://github.com/nicolaisi/fm-breakeven.