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Generating probabilistic forecasts from arbitrary point forecasts using a conditional invertible neural network
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DOI：10.1007/s10489-024-05346-9.png)
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Abstract
En 中文
In various applications, probabilistic forecasts are required to quantify the inherent uncertainty associated with the forecast. However, many existing forecasting methods still only generate point forecasts. Although methods exist to generate probabilistic forecasts from these point forecasts, these are often limited to prediction intervals or must be trained together with a specific point forecast. Therefore, the present article proposes a novel approach for generating probabilistic forecasts from arbitrary point forecasts. In order to implement this approach, we apply 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 point forecast to generate probabilistic forecasts. We evaluate our approach by generating probabilistic forecasts from multiple point forecasts and comparing these forecasts to six probabilistic benchmarks on four data sets. We show that our approach generally outperforms all benchmarks with regard to CRPS and Winkler scores and generates probabilistic forecasts with the narrowest prediction intervals whilst remaining reasonably calibrated. Furthermore, our approach enables simple point forecasting methods to rank highly in the Global Energy Forecasting Competition 2014.
Keywords:
Probabilistic forecasting
Uncertainty quantification
Conditional invertible neural networks
Machine learning
Normalising flows