Instance-Adaptive Prompts as Context for Time-Series Foundation Models

arXiv:2609.34786 · cs.LG · Submitted 2026-09-28 · Read on arXiv

cs.LG

Submitted: 2026-09-28

Updated: 2026-09-28

Code: https://github.com/zzzx1224/PaCTS

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

Terminology

• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes

Sources

• GPT-4 Technical Report
• GIFT-Eval: A Benchmark For General Time Series Forecasting Model Evaluation
• 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
• On the Opportunities and Risks of Foundation Models
• Compositional Spectral Prompts for LLM-based Online Time Series Forecasting
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• Beyond LoRA: Exploring Efficient Fine-Tuning Techniques for Time Series Foundational Models
• Toto 2.0: Time Series Forecasting Enters the Scaling Era
• Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution
• Foundation Models and Fine-Tuning: Toward a New Generation of Models for Time Series Forecasting
• TimeRFT: Stimulating Generalizable Time Series Forecasting for TSFMs via Reinforcement Finetuning
• DeepSeek-V3 Technical Report
• Moirai 2.0: When Less Is More for Time Series Forecasting
• Timer: Generative Pre-trained Transformers Are Large Time Series Models
• Timer-S1: A Billion-Scale Time Series Foundation Model with Serial Scaling
• t 0: A Time-Series Foundation Model for Forecasting with Context
• TimeHF: Billion-Scale Time Series Models Guided by Human Feedback
• It's TIME: Towards the Next Generation of Time Series Forecasting Benchmarks

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