# Calibration-cum-resolution

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).

### Bibliography

- Vladimir Vovk, Non-asymptotic calibration and resolution.
*Theoretical Computer Science*(Special Issue devoted to ALT 2005)**387**, 77–89 (2007).