Agentic data science with the Data Agent Kit
cloud.google.com·7h ago
AI Research
arXiv CS.AI·12d ago·5 min read
JudgeCast: Time Series Forecasting with Experience-Informed Covariate Judgements
TL;DR
JudgeCast is a novel framework for time series forecasting that leverages experience-informed adjustments of covariate effects. It outperforms traditional methods by using past observations to refine future forecasts.
✦ Why It Matters
Implement JudgeCast to enhance your time series forecasting accuracy using past performance insights.
Key Takeaways
- Utilize JudgeCast for improved time series predictions.
- Incorporate explicit covariate judgments in your forecasting process.
- Leverage past forecast errors to refine future predictions.
- Test JudgeCast on diverse datasets for robust performance.
How It Works
JudgeCast operates by first generating a base forecast using a frozen TSFM, then applying adjustments informed by a frozen LLM that considers the current context and past experiences. It explicitly assesses the effects of each covariate, allowing for tailored adjustments rather than a one-size-fits-all approach.
After observing the actual outcomes, it reconstructs alternative judgments based on the residuals of the base forecast, evaluating which adjustments yield the best results for future predictions.