In-Context Inpainting for Time Series Forecasting

arXiv:2608.23855 · cs.AI · Submitted 2026-08-24 · Read on arXiv

cs.AI

Submitted: 2026-08-24

Updated: 2026-08-24

Code: https://github.com/zhouhaoyi/ETDataset

Terminology

• Time Series Forecasting — 25× in this paper · explained in 3 episodes
• Transformer — 15× in this paper · explained in 2 episodes
• In-Context Learning — 14× in this paper · explained in 4 episodes
• Fine-Tuning — 7× in this paper · explained in 5 episodes
• Adaptation — 6× in this paper · explained in 1 episode
• Foundation Model — 6× in this paper · explained in 3 episodes
• Foundation Models — 4× in this paper · explained in 7 episodes
• Visual In-Context Learning — 4× in this paper
• Ablation — 3× in this paper · explained in 1 episode
• Adaptability — 3× in this paper · explained in 1 episode
• In-Context Learning (ICL) — 3× in this paper · explained in 5 episodes
• International Conference on Learning Representations — 3× in this paper
• International Conference on Machine Learning — 3× in this paper
• Accuracy — 2× in this paper · explained in 1 episode
• Consistency — 2× in this paper · explained in 3 episodes
• International Joint Conference on Artificial Intelligence — 2× in this paper
• Modality Gap — 2× in this paper · explained in 4 episodes
• Architectural Adaptation — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Chain-of-Thought Prompting — 1× in this paper · explained in 2 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Mean Absolute Error — 1× in this paper
• Mean Squared Error — 1× in this paper
• Probabilistic Forecasting — 1× in this paper · explained in 3 episodes
• Quantization — 1× in this paper · explained in 12 episodes

Sources

• ChartLlama: A Multimodal LLM for Chart Understanding and Generation
• OccamVTS: Distilling Vision Models to 1% Parameters for Time Series Forecasting
• SVTime: Small Time Series Forecasting Models Informed by "Physics" of Large Vision Model Forecasters
• VisionTS++: Cross-Modal Time Series Foundation Model with Continual Pre-trained Vision Backbones
• Large Language Models for Forecasting and Anomaly Detection: A Systematic Literature Review
• Empowering Time Series Analysis with Foundation Models: A Comprehensive Survey

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