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arXiv CS.AI·1d ago·5 min read
ChronoScribe: Semantic-Numerical Specialization for Instruction-Driven Time Series Forecasting
TL;DR
ChronoScribe is a novel framework that combines large language models (LLMs) and time series foundation models (TSFMs) for improved time series forecasting. It outperforms existing systems, achieving the lowest error rates across multiple metrics.
✦ Why It Matters
Consider implementing ChronoScribe for your time series forecasting tasks to achieve superior accuracy.
Key Takeaways
- Utilize ChronoScribe for enhanced time series forecasting accuracy.
- Leverage LLMs for natural language processing in forecasting tasks.
- Implement deterministic rules for configuration in time series models.
- Evaluate performance using metrics like RMSE and MAE.
How It Works
ChronoScribe combines a LoRA-adapted LLM for interpreting natural language instructions with a frozen TSFM for generating numerical forecasts. The LLM proposes an execution path and context length, while deterministic rules finalize the configuration.
The TSFM then produces quantile trajectories, which are used to summarize the forecast without the LLM generating or revising numerical values.