Calibrated Probabilistic Forecasts for Arbitrary Sequences.

Area of Science:

• Machine Learning
• Game Theory
• Data Science

Background:

• Real-world data streams face unpredictable changes (distribution shifts, feedback loops, adversarial actors).
• These changes challenge the validity and reliability of existing forecasting methods.
• Ensuring accurate uncertainty estimates is crucial for robust decision-making.

Purpose of the Study:

• To develop a forecasting framework that provides valid uncertainty estimates irrespective of data evolution.
• To guarantee calibrated uncertainties for outcomes in compact spaces.
• To extend the framework for recalibrating existing forecasters without performance loss.

Main Methods:

• Leveraging Blackwell approachability from game theory.
• Developing a general-purpose gradient-based algorithm.
• Optimizing algorithms for special cases of the framework.
• Implementing recalibration techniques for existing forecasters.

Main Results:

• The framework guarantees calibrated uncertainties for any compact outcome space.
• Recalibrated forecasters achieve calibration without sacrificing predictive performance.
• Empirical results demonstrate improved calibration and decision-making in energy systems.

Conclusions:

• The proposed framework ensures valid uncertainty estimates in dynamic data environments.
• This approach enhances the reliability of forecasts and downstream decision-making.
• The methods are applicable to various forecasting tasks, including classification and bounded regression.