Continuous Ranked Probability Score  CRPS
The Continuous Ranked Probability Score, known as CRPS, is a score to measure how a proposed distribution approximates the data, without knowledge about the true distributions of the data.
Definition
CRPS is defined as^{1}
$$ CRPS(P, x_a) = \int_{\infty}^\infty \lVert P(x)  H(x  x_a) \rVert_2 dx, $$
where
 $x_a$ is the true value of $x$,
 P(x) is our proposed cumulative distribution for $x$,
 $H(x)$ is the Heaviside step function
$$ H(x) = \begin{cases} 1, &\qquad x=0\\ 0, &\qquad x\leq 0\\ \end{cases} $$
 $\lVert \cdot \rVert_2$ is the L2 norm.
Explain it
The formula looks abstract on first sight, but it becomes crystal clear once we understand it.
Note that the distributions that corresponds to a Heaviside CDF is the delta function $\delta(xx_a)$. What this score is calculating is the difference between our distribution and a delta function. If we have a model that minimizes CRPS, then we are looking for a distribution that is close to the delta function $\delta(xx_a)$. In other words, we want our distribution to be large around $x_a$.
To illustrate what the integrand $\lVert P(x)  H(x  x_a) \rVert_2$ means, we consider several scenarios.
The shade areas determines the integrand of the integral in CRPS. The only way to get a small score is to choose a distribution that is focused around $x_a$.
Compared to fDivergence
Compared to [[KL Divergence]] KL Divergence Kullback–Leibler divergence indicates the differences between two distributions or more generally [[fDivergence]] fDivergence The fdivergence is defined as1 $$ \operatorname{D}_f = \int f\left(\frac{p}{q}\right) q\mathrm d\mu, $$ where $p$ and $q$ are two densities and $\mu$ is a reference distribution. Requirements on the generating function The generating function $f$ is required to be convex, and $f(1) =0$. For $f(x) = x \log x$ with $x=p/q$, fdivergence is reduced to the KL divergence $$ \begin{align} &\int f\left(\frac{p}{q}\right) q\mathrm d\mu \\ =& \int \frac{p}{q} \log \left( \frac{p}{q} \right) \mathrm … , CRPS is comparing our proposed CDF to the Heaviside CDF.
Compared to Likelihood
Gebetsberger et al found that CRPS is more robust but produces similar results if we have found a good assumption about the data distribution^{2}.
Applications
One quite interesting application of the CRPS is to write down the loss for an the quantile function^{3}.

Hersbach2000 Hersbach H. Decomposition of the Continuous Ranked Probability Score for Ensemble Prediction Systems. Weather Forecast. 2000;15: 559–570. doi:10.1175/15200434(2000)015<0559:DOTCRP>2.0.CO;2 ↩︎

Gebetsberger2018 Gebetsberger M, Messner JW, Mayr GJ, Zeileis A. Estimation Methods for Nonhomogeneous Regression Models: Minimum Continuous Ranked Probability Score versus Maximum Likelihood. Mon Weather Rev. 2018;146: 4323–4338. doi:10.1175/MWRD170364.1 ↩︎

Gouttes2021 Gouttes A, Rasul K, Koren M, Stephan J, Naghibi T. Probabilistic Time Series Forecasting with Implicit Quantile Networks. arXiv [cs.LG]. 2021. doi:10.1109/icdmw.2017.19 ↩︎
 Hersbach2000 Hersbach H. Decomposition of the Continuous Ranked Probability Score for Ensemble Prediction Systems. Weather Forecast. 2000;15: 559–570. doi:10.1175/15200434(2000)015<0559:DOTCRP>2.0.CO;2
 Gouttes2021 Gouttes A, Rasul K, Koren M, Stephan J, Naghibi T. Probabilistic Time Series Forecasting with Implicit Quantile Networks. arXiv [cs.LG]. 2021. doi:10.1109/icdmw.2017.19
 Gebetsberger2018 Gebetsberger M, Messner JW, Mayr GJ, Zeileis A. Estimation Methods for Nonhomogeneous Regression Models: Minimum Continuous Ranked Probability Score versus Maximum Likelihood. Mon Weather Rev. 2018;146: 4323–4338. doi:10.1175/MWRD170364.1
L Ma (2022). 'Continuous Ranked Probability Score  CRPS', Datumorphism, 03 April. Available at: https://datumorphism.leima.is/cards/timeseries/crps/.