  Forecasting and forecast evaluation are inherently sequential tasks.
Predictions are often issued on a regular basis, such as every hour, day, or
month, and their quality is monitored continuously. However, the classical
statistical tools for forecast evaluation are static, in the sense that
statistical tests for forecast calibration are only valid if the evaluation
period is fixed in advance. Recently, e-values have been introduced as a new,
dynamic method for assessing statistical significance. An e-value is a
non-negative random variable with expected value at most one under a null
hypothesis. Large e-values give evidence against the null hypothesis, and the
multiplicative inverse of an e-value is a conservative p-value. E-values are
particularly suitable for sequential forecast evaluation, since they naturally
lead to statistical tests which are valid under optional stopping. This article
proposes e-values for testing probabilistic calibration of forecasts, which is
one of the most important notions of calibration. The proposed methods are also
more generally applicable for sequential goodness-of-fit testing. We
demonstrate that the e-values are competitive in terms of power when compared
to extant methods, which do not allow sequential testing. Furthermore, they
provide important and useful insights in the evaluation of probabilistic
weather forecasts.
