KimMeen/Time-LLM

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2685 stars · 466 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Time-LLM is the official PyTorch implementation of an ICLR 2024 paper that reprograms frozen large language models (Llama, GPT-2, BERT) for general time series forecasting. It aligns time series data with text prototypes and uses Prompt-as-Prefix to leverage LLM reasoning without modifying the backbone model.

Use cases

• forecast time series with large language models
• reprogram an LLM for time series prediction
• reproduce the Time-LLM ICLR 2024 paper results
• use Llama or GPT-2 as a time series forecasting backbone
• apply prompt-based forecasting to energy or weather data
• compare LLM-based forecasting baselines

When to choose

• you want to leverage pretrained LLMs for forecasting without training the backbone
• you need a research-grade implementation of the Time-LLM method
• you want to experiment with cross-modal reprogramming and prompt-as-prefix techniques

When to avoid

• you need lightweight, low-latency production forecasting without GPU-heavy LLM inference
• you want simple classical or dedicated time series models
• you need a maintained library API rather than research code

Facets

library · maturity active

machine-learning deep-learning llm-inference time-series large-language-models machine-learning deep-learning python time-series-forecasting llm-reprogramming prompt-as-prefix iclr-2024 multimodal research-code

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