Report
Calibrated Probabilistic Forecasts for Arbitrary Sequences
| العنوان: | Calibrated Probabilistic Forecasts for Arbitrary Sequences |
|---|---|
| المؤلفون: | Marx, Charles, Kuleshov, Volodymyr, Ermon, Stefano |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Statistics - Machine Learning |
| الوصف: | Real-world data streams can change unpredictably due to distribution shifts, feedback loops and adversarial actors, which challenges the validity of forecasts. We present a forecasting framework ensuring valid uncertainty estimates regardless of how data evolves. Leveraging the concept of Blackwell approachability from game theory, we introduce a forecasting framework that guarantees calibrated uncertainties for outcomes in any compact space (e.g., classification or bounded regression). We extend this framework to recalibrate existing forecasters, guaranteeing accurate uncertainties without sacrificing predictive performance. We implement both general-purpose gradient-based algorithms and algorithms optimized for popular special cases of our framework. Empirically, our algorithms improve calibration and downstream decision-making for energy systems. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2409.19157 |
| رقم الانضمام: | edsarx.2409.19157 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |