Creating Probabilistic Forecasts from Arbitrary Deterministic Forecasts
using Conditional Invertible Neural Networks
- URL: http://arxiv.org/abs/2302.01800v1
- Date: Fri, 3 Feb 2023 15:11:39 GMT
- Title: Creating Probabilistic Forecasts from Arbitrary Deterministic Forecasts
using Conditional Invertible Neural Networks
- Authors: Kaleb Phipps and Benedikt Heidrich and Marian Turowski and Moritz
Wittig and Ralf Mikut and Veit Hagenmeyer
- Abstract summary: We use a conditional Invertible Neural Network (cINN) to learn the underlying distribution of the data and then combine the uncertainty from this distribution with an arbitrary deterministic forecast.
Our approach enables the simple creation of probabilistic forecasts without complicated statistical loss functions or further assumptions.
- Score: 0.19573380763700712
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In various applications, probabilistic forecasts are required to quantify the
inherent uncertainty associated with the forecast. However, numerous modern
forecasting methods are still designed to create deterministic forecasts.
Transforming these deterministic forecasts into probabilistic forecasts is
often challenging and based on numerous assumptions that may not hold in
real-world situations. Therefore, the present article proposes a novel approach
for creating probabilistic forecasts from arbitrary deterministic forecasts. In
order to implement this approach, we use a conditional Invertible Neural
Network (cINN). More specifically, we apply a cINN to learn the underlying
distribution of the data and then combine the uncertainty from this
distribution with an arbitrary deterministic forecast to generate accurate
probabilistic forecasts. Our approach enables the simple creation of
probabilistic forecasts without complicated statistical loss functions or
further assumptions. Besides showing the mathematical validity of our approach,
we empirically show that our approach noticeably outperforms traditional
methods for including uncertainty in deterministic forecasts and generally
outperforms state-of-the-art probabilistic forecasting benchmarks.
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