Third-party cyber evaluations involving OpenAI models
openai.com·14h ago
AI Research
arXiv CS.AI·Jun 10·5 min read
Mix, Don't Pick: Why Synthetic Corpus Composition Matters for Time Series Foundation Model Pretraining
TL;DR
Choosing the right synthetic data generator for time-series model pretraining is challenging and can significantly impact forecasting accuracy. Instead of selecting a single generator, a mixture of all evaluated generators was found to perform better than the best individual generator.
✦ Why It Matters
Engineers can improve time-series model performance by using a mixture of synthetic data generators instead of selecting a single one.
Key Takeaways
- Evaluate multiple synthetic data generators for better model performance.
- Use a mixture of generators instead of selecting a single one.
- Combine synthetic data with real data for optimal results.
- Tailor your approach based on the specific model architecture.
How It Works
The study demonstrates that an equal-weight mixture of all evaluated synthetic data generators can outperform the best individual generator. This method leverages the strengths of diverse data sources, leading to improved forecasting accuracy across different model architectures.