Calibration-cum-resolution is the property of forecasts that unites the calibration and resolution properties. Let the sequence of outcomes be $y_n$ (assumed binary), the sequence of forecasts be $\gamma_n$, and let $x_n$ be the signal used in forecasting $y_n$. The forecasts have this property if

$\frac{\sum_{n=1,\dots,N: (\gamma_n,x_n) \approx (\gamma^*,x^*)} (y_n - \gamma_n) } { \sum_{n=1,\dots,N: (\gamma_n,x_n) \approx (\gamma^*,x^*)} 1 }\approx 0,$

for all forecasts $\gamma^*$ and all signals $x^*$. A convenient (and easier to formalize) restatement of this property is: a prediction algorithm achieves asymptotic calibration-cum-resolution if

$\lim\limits_{N \to \infty} \frac{1}{N} \sum_{n=1}^N (y_n - \gamma_n) f(\gamma_n, x_n) = 0$

for all continuous functions $f: [0,1] \times X \to \mathbb{R}$ from some class. Calibration corresponds to the case where $f=f(\gamma,x)$ does not depend on $x$, and resolution to the case where $f$ does not depend on $\gamma$. In case of weather forecasts, calibration-cum-resolution means that forecaster is good in predicting of the probability of rain (it was raining in 70% of the days, when the forecaster predicted 70% probability of rain), and he is also good in predicting the weather "for Thursdays" (or for any other days, if we assess his forecasts only for these days).


  • Vladimir Vovk, Non-asymptotic calibration and resolution. Theoretical Computer Science (Special Issue devoted to ALT 2005) 387, 7789 (2007).