Agentic data science with the Data Agent Kit
cloud.google.com·6h ago
AI Research
arXiv CS.AI·27d ago·5 min read
CodeTS: Verifiable Text-to-Time Series Generation via Executable Code
TL;DR
CodeTS is a novel framework that transforms natural language descriptions into time series data by using executable code as an intermediary. It outperforms existing methods, providing a robust zero-shot solution for generating time series from text.
✦ Why It Matters
Explore CodeTS to enhance your time series generation capabilities from textual descriptions today.
Key Takeaways
- Utilize CodeTS for efficient time series generation from text.
- Leverage executable code to improve generation accuracy.
- Implement multi-stage rewards for better model training.
- Experiment with CodeTS across different time series benchmarks.
How It Works
CodeTS reformulates the Text-to-TS process into a Text-to-Code-to-TS framework. It first translates textual temporal descriptions into executable code, which defines how the text influences the resulting time series.
The generated code is then executed to produce the final time series output. This approach allows for a clear mapping from text to code, ensuring that the generated time series aligns with the original textual requirements.