TS2TabPFN: Time Series Classification and Extrinsic Regression through Feature Extraction and a Tabular Foundation Model

arXiv:2608.04174 · cs.LG · Submitted 2026-08-04 · Read on arXiv

cs.LG

Submitted: 2026-08-04

Updated: 2026-08-04

Comments: Accepted at ECML PKDD 2026. To appear in Springer LNCS. 18 pages; 7 figures

Journal ref: Machine Learning and Knowledge Discovery in Databases. Research Track. ECML PKDD 2026. Lecture Notes in Computer Science, vol. 16947. Springer, Cham, 2027

DOI: 10.1007/978-3-032-37676-3_32

Code: https://github.com/gabrielcmerlin/TS2TabPFNhttps:

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Feature Extraction — 26× in this paper · explained in 2 episodes
• Benchmark — 22× in this paper · explained in 2 episodes
• Foundation Model — 21× in this paper · explained in 3 episodes
• Time Series Classification — 14× in this paper
• Accuracy — 11× in this paper · explained in 1 episode
• Foundation Models — 11× in this paper · explained in 7 episodes
• Transformer — 9× in this paper · explained in 2 episodes
• Core — 6× in this paper · explained in 2 episodes
• Critical Difference — 5× in this paper
• Feature Engineering — 4× in this paper · explained in 3 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Alignment — 2× in this paper · explained in 1 episode
• Benchmarking — 2× in this paper · explained in 3 episodes
• Decoupling — 2× in this paper · explained in 2 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Adaptability — 1× in this paper · explained in 1 episode
• Adhering to the Prior-Data Fitted Network — 1× in this paper
• Aggregation — 1× in this paper · explained in 1 episode
• Bayesian Inference — 1× in this paper · explained in 5 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Diversified Random Convolutional Interval Forest — 1× in this paper
• Ensemble Approach — 1× in this paper · explained in 3 episodes
• In-Context Learning — 1× in this paper · explained in 4 episodes
• In-Context Learning (ICL) — 1× in this paper · explained in 5 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Pairwise Comparisons — 1× in this paper · explained in 2 episodes
• Physical Constraints — 1× in this paper · explained in 7 episodes
• Posterior Predictive Distribution — 1× in this paper
• Root Mean Squared Error — 1× in this paper
• Time Series Forecasting — 1× in this paper · explained in 3 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes

Sources

• Is rotation forest the best classifier for problems with continuous features?
• Mantis: Lightweight Foundation Model for Time Series Classification
• TabPFN-2.5: Advancing the State of the Art in Tabular Foundation Models
• Quantifying the Carbon Emissions of Machine Learning

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: Local-Consistency Optimisation for Bayesian Neural Networks
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks