A Foundation Model for Instruction-Conditioned In-Context Time Series Tasks

arXiv:2603.22586 · cs.LG · Submitted 2026-03-23 · Read on arXiv

cs.LG

Submitted: 2026-03-23

Updated: 2026-09-22

Terminology

• Adaptation — 36× in this paper · explained in 1 episode
• Benchmark — 30× in this paper · explained in 2 episodes
• Foundation Model — 22× in this paper · explained in 3 episodes
• Accuracy — 21× in this paper · explained in 1 episode
• In-Context Learning — 19× in this paper · explained in 4 episodes
• Meta-learning — 17× in this paper · explained in 2 episodes
• Foundation Models — 15× in this paper · explained in 7 episodes
• Ablation — 14× in this paper · explained in 1 episode
• Transformer — 14× in this paper · explained in 2 episodes
• Embeddings — 11× in this paper · explained in 2 episodes
• Robustness — 8× in this paper · explained in 5 episodes
• Time Series Forecasting — 8× in this paper · explained in 3 episodes
• Distribution Shift — 7× in this paper · explained in 5 episodes
• Generalization — 7× in this paper · explained in 13 episodes
• Linear probe — 6× in this paper · explained in 2 episodes
• Fine-Tuning — 5× in this paper · explained in 5 episodes
• Alignment — 4× in this paper · explained in 1 episode
• In-Context Learning (ICL) — 4× in this paper · explained in 5 episodes
• Tokenization — 4× in this paper · explained in 2 episodes
• Calibration — 3× in this paper · explained in 7 episodes
• Probabilistic Forecasting — 3× in this paper · explained in 3 episodes
• Trace — 3× in this paper · explained in 2 episodes
• Zero-Shot Generalization — 3× in this paper · explained in 5 episodes
• Attention Mechanism — 2× in this paper · explained in 2 episodes
• Clustering — 2× in this paper · explained in 2 episodes
• Data Augmentation — 2× in this paper · explained in 7 episodes
• Information Leakage — 2× in this paper · explained in 2 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Core — 1× in this paper · explained in 2 episodes
• F1 Score — 1× in this paper · explained in 3 episodes
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• Mixture-of-Experts (MoE) — 1× in this paper · explained in 7 episodes
• Model-Agnostic — 1× in this paper · explained in 2 episodes
• Pairwise Comparisons — 1× in this paper · explained in 2 episodes
• Structured Reasoning — 1× in this paper · explained in 3 episodes
• Synthetic Data Generation — 1× in this paper · explained in 5 episodes
• Transferability — 1× in this paper · explained in 4 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes
• Zero-Shot Transfer — 1× in this paper · explained in 4 episodes

Sources

• GIFT-Eval: A Benchmark For General Time Series Forecasting Model Evaluation
• Understanding intermediate layers using linear classifier probes
• Chronos: Learning the Language of Time Series
• Chronos-2: From Univariate to Universal Forecasting
• TiRex: Zero-Shot Forecasting Across Long and Short Horizons with Enhanced In-Context Learning
• The UEA multivariate time series classification archive, 2018
• This Time is Different: An Observability Perspective on Time Series Foundation Models
• In-Context Fine-Tuning for Time-Series Foundation Models
• ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels
• Deep learning for time series classification
• Surface Form Competition: Why the Highest Probability Answer Isn't Always Right
• From Tables to Time: Extending TabPFN-v2 to Time Series Forecasting
• In-context Time Series Predictor
• A Time Series is Worth 64 Words: Long-term Forecasting with Transformers
• N-BEATS: Neural basis expansion analysis for interpretable time series forecasting
• An Overview of Multi-Task Learning in Deep Neural Networks
• fev-bench: A Realistic Benchmark for Time Series Forecasting
• Llama 2: Open Foundation and Fine-Tuned Chat Models
• Finetuned Language Models Are Zero-Shot Learners
• When Do Curricula Work?

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