Quantile Regression and Clustering Models of Prediction Intervals for Weather Forecasts: A Comparative Study

Quantile Regression and Clustering Models of Prediction Intervals for Weather Forecasts: A Comparative Study

Information about forecast uncertainty is vital for optimal decision making in many domains that use weather forecasts. However, it is not available in the immediate output of deterministic numerical weather prediction systems. In this paper, we investigate several learning methods to train and eval...

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Bibliographic Details
Main Authors: Ashkan Zarnani, Soheila Karimi, Petr Musilek
Format: Article
Language:English
Published: MDPI AG 2019-10-01
Series:Forecasting
Subjects:
data clustering
forecast verification
fuzzy clustering
prediction intervals
probabilistic forecast
quantile regression
uncertainty modeling
weather forecasting
Online Access:https://www.mdpi.com/2571-9394/1/1/12

Internet

https://www.mdpi.com/2571-9394/1/1/12

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