TMAML: Temporal Model-Agnostic Meta-Learning for Cold-Start Time Series Forecasting

arXiv:2610.04547 · cs.LG · Submitted 2026-10-03 · Read on arXiv

cs.LG

Submitted: 2026-10-03

Updated: 2026-10-03

Code: https://github.com/Wannes15/TMAML_tslimits

Terminology

• Meta-learning — 1× in this paper · explained in 2 episodes
• Model-Agnostic — 1× in this paper · explained in 2 episodes
• Time Series Forecasting — 1× in this paper · explained in 3 episodes

Sources

• Chronos: Learning the Language of Time Series
• Sundial: A Family of Highly Capable Time Series Foundation Models
• TiRex: Zero-Shot Forecasting Across Long and Short Horizons with Enhanced In-Context Learning
• N-BEATS: Neural basis expansion analysis for interpretable time series forecasting
• A Time Series is Worth 64 Words: Long-term Forecasting with Transformers

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: The Predictive Cost of ELBO Binding
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks