# Online Aggregation of Probability Forecasts with Confidence

arXiv: https://arxiv.org/abs/2109.14309 · HTML: https://mlsanity.com/paper/2109.14309/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 32 references that are in the map.
- First posted on arXiv 29 Sep 2021.

## Builds on (most cited first, 5 of 5)

- [A Second-order Bound with Excess Losses](https://mlsanity.com/paper/1402.2044/) (2014)
- [Mixability of Integral Losses: a Key to Efficient Online Aggregation of Functional and Probabilistic Forecasts](https://mlsanity.com/paper/1912.07048/) (2019)
- [Online Learning with Continuous Ranked Probability Score](https://mlsanity.com/paper/1902.10173/) (2019)
- [Aggregating Algorithm for Prediction of Packs](https://mlsanity.com/paper/1710.08114/) (2017)
- [Online Aggregation of Unbounded Losses Using Shifting Experts with Confidence](https://mlsanity.com/paper/1808.00741/) (2018)

## Built on by (most cited first, 3 of 3)

- [Multivariate Probabilistic CRPS Learning with an Application to Day-Ahead Electricity Prices](https://mlsanity.com/paper/2303.10019/) (2023)
- [Temporal distribution of clusters of investors and their application in prediction with expert advice](https://mlsanity.com/paper/2406.19403/) (2024)
- [AdaWeather: Adaptively Mixing Probabilistic Weather Forecasts with Logarithmic Regret](https://mlsanity.com/paper/2606.02663/) (2026)

## Abstract (arXiv)

The paper presents numerical experiments and some theoretical developments in prediction with expert advice (PEA). One experiment deals with predicting electricity consumption depending on temperature and uses real data. As the pattern of dependence can change with season and time of the day, the domain naturally admits PEA formulation with experts having different ``areas of expertise''. We consider the case where several competing methods produce online predictions in the form of probability distribution functions. The dissimilarity between a probability forecast and an outcome is measured by a loss function (scoring rule). A popular example of scoring rule for continuous outcomes is Continuous Ranked Probability Score (CRPS). In this paper the problem of combining probabilistic forecasts is considered in the PEA framework. We show that CRPS is a mixable loss function and then the time-independent upper bound for the regret of the Vovk aggregating algorithm using CRPS as a loss function can be obtained. Also, we incorporate a ``smooth'' version of the method of specialized experts in this scheme which allows us to combine the probabilistic predictions of the specialized experts with overlapping domains of their competence.