Research Track
Research papers presenting novel theoretical insights, methodological advances, or empirical findings related to time series foundation models; max 4 pages.
Machine Learning/CORE Unranked
ICLR 2026
1st ICLR Workshop on Time Series in the Age of Large Models
The ICLR 2026 Workshop on Time Series in the Age of Large Models (TSALM) focuses on advancing time series foundation models by addressing key challenges in context-informed prediction, reasoning with LLM-powered agents, interpretability, and rigorous evaluation. It brings together researchers and practitioners to share innovations in multimodal forecasting, agentic workflows, benchmarks, and real-world applications across domains like healthcare, finance, and climate science.
Key deadlines
Verified September 9, 2026Full paper
February 15, 2026
AoE
Workshop timeline
Submission and decisions
Full paperKey deadline
February 15, 2026 · AoE
Paper fit
Contribution paths
A strong submission should clearly identify its contribution and evaluate it appropriately.
Industry & Applications Track
Submissions from industry practitioners, open-source contributors, and applied researchers prioritizing practical impact and real-world insights; max 2 pages. May include implementations, re-analyses, critiques, deployment experiences, open-source tools, or large-scale applications.
Research areas in scope
01
Context-informed foundation models
Multivariate forecastingStatic and dynamic exogenous variablesMultimodal time seriesAnomaly detectionClassification
02
Reasoning and agents
LLM-powered agents for time series tasksAgentic scaffoldsBenchmarks for evaluating agent performanceReasoning capabilities in time series models (explicit and implicit)
03
Benchmarks, datasets, and tools
Unified evaluation frameworksLarge-scale and live benchmarksSynthetic data generationOpen-source libraries for pretraining and inference
04
Interpretability
Attention analysisFeature attributionSymbolic distillationCausal interpretabilityScalability of interpretability methods with model size
05
Evaluation and applications
Metrics for probabilistic predictionDomain-specific evaluationApplications in finance, healthcare, climate science, and dynamical systems
Policies worth checking twice
- Submissions must be double-blind and properly anonymized; non-anonymized or over-length papers will be desk-rejected.
- Submissions are limited to 2 or 4 pages (main text), with additional pages allowed for references and appendices (reviewers not required to read appendices).
- Dual submission is permitted: papers under review elsewhere or previously published may be submitted if reasonably extended.
- The workshop is non-archival; accepted papers will be available on OpenReview but have no formally published proceedings.
- Authors must comply with ICLR 2026’s LLM use policy for authors.
Official sources
Compiled from the official call for papers. The organizers’ pages remain authoritative.
Official workshop siteCall for PapersAccepted PapersNeurIPS 2024 workshopthis LaTeX style templateCall for Papers
Last verified September 9, 2026