Probabilistic Recalibration of Forecasts

04/05/2019
by   Carlo Graziani, et al.
0

We present a scheme by which a probabilistic forecasting system whose predictions have poor probabilistic calibration may be recalibrated by incorporating past performance information to produce a new forecasting system that is demonstrably superior to the original, in that one may use it to consistently win wagers against someone using the original system. The scheme utilizes Gaussian process (GP) modeling to estimate a probability distribution over the Probability Integral Transform (PIT) of a scalar predictand. The GP density estimate gives closed-form access to information entropy measures associated with the estimated distribution, which allows prediction of winnings in wagers against the base forecasting system. A separate consequence of the procedure is that the recalibrated forecast has a uniform expected PIT distribution. A distinguishing feature of the procedure is that it is appropriate even if the PIT values are not i.i.d. The recalibration scheme is formulated in a framework that exploits the deep connections between information theory, forecasting, and betting. We demonstrate the effectiveness of the scheme in two case studies: a laboratory experiment with a nonlinear circuit and seasonal forecasts of the intensity of the El Niño-Southern Oscillation phenomenon.

READ FULL TEXT
research
06/02/2016

Forecasting Framework for Open Access Time Series in Energy

In this paper we propose a framework for automated forecasting of energy...
research
01/17/2021

Post-processing numerical weather prediction ensembles for probabilistic solar irradiance forecasting

In order to enable the transition towards renewable energy sources, prob...
research
06/01/2022

Why Did This Model Forecast This Future? Closed-Form Temporal Saliency Towards Causal Explanations of Probabilistic Forecasts

Forecasting tasks surrounding the dynamics of low-level human behavior a...
research
07/21/2020

A Comparison of Aggregation Methods for Probabilistic Forecasts of COVID-19 Mortality in the United States

The COVID-19 pandemic has placed forecasting models at the forefront of ...
research
03/23/2022

A Deep Learning Approach to Probabilistic Forecasting of Weather

We discuss an approach to probabilistic forecasting based on two chained...
research
04/27/2018

Event Forecasting with Pattern Markov Chains

We present a system for online probabilistic event forecasting. We assum...
research
03/24/2020

Probabilistic forecasting approaches for extreme NO_2 episodes: a comparison of models

High concentration episodes for NO_2 are increasingly dealt with by auth...

Please sign up or login with your details

Forgot password? Click here to reset