Live briefing
Everything about Probabilistic forecasting
Type of statistical forecasting
Probabilistic forecasting summarizes what is known about, or opinions about, future events. In contrast to single-valued forecasts, probabilistic forecasts assign a probability to each of a number of different outcomes, and the complete set of probabilities represents a probability forecast. Thus, probabilistic forecasting is a type of probabilistic classification. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Probabilistic forecastingA 5-minute orientation
- Skim a recent paper: Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United StatesInfluential work since 2022
- Pick up a classic book: An efficient deterministic-probabilistic approach to modeling regional ground-water flowThe most-reprinted book on the subject
- Read the landmark paper: DeepAR: Probabilistic forecasting with autoregressive recurrent networksCited 2,506 times
- Go sideways: Ensemble forecastingA closely related topic
- Go sideways: Quantile regression averagingA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
DeepAR: Probabilistic forecasting with autoregressive recurrent networks
Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond
Probabilistic Forecasting
Recent advances in electricity price forecasting: A review of probabilistic forecasting
Probabilistic weather forecasting with machine learning
Probabilistic forecasting of earthquakes
Global energy forecasting competition 2017: Hierarchical probabilistic load forecasting
Bayesian system for probabilistic river stage forecasting
🆕 Influential since 2022
Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States
Probabilistic solar irradiance forecasting based on XGBoost
Probabilistic population forecasting: Short to very long-term
Probabilistic solar forecasting: Benchmarks, post-processing, verification
Parameter-efficient deep probabilistic forecasting
📚 Books worth reading
- 📕An efficient deterministic-probabilistic approach to modeling regional ground-water flowC. C. Yen · 1988
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Ensemble forecasting Ensemble forecasting is a method used in or within numerical weather prediction. Instead of making a single forecast of the most likely weather, a set…
- Quantile regression averaging Quantile Regression Averaging (QRA) is a forecast combination approach to the computation of prediction intervals. It involves applying quantile regression…
- Scoring rule In decision theory, both a scoring rule as well as a scoring function provide an ex post summary measure for the evaluation of the quality of a prediction…
- Consensus forecast A consensus forecast is a prediction of the future created by combining several separate forecasts which have often been created using different methodologies…
- Nonhomogeneous Gaussian regression Non-homogeneous Gaussian regression (NGR) is a type of statistical regression analysis used in the atmospheric sciences as a way to convert ensemble forecasts…
- Quantitative precipitation forecast The quantitative precipitation forecast (abbreviated QPF) is the expected amount of melted precipitation accumulated over a specified time period over…
- Electricity price forecasting Electricity price forecasting (EPF) is a branch of energy forecasting which focuses on using mathematical, statistical and machine learning models to predict…
- Tropical cyclone forecast model A tropical cyclone forecast model is a computer program that uses meteorological data to forecast aspects of the future state of tropical cyclones. There…