  As the popularity of hierarchical point forecast reconciliation methods
increases, there is a growing interest in probabilistic forecast
reconciliation. Many studies have utilized machine learning or deep learning
techniques to implement probabilistic forecasting reconciliation and have made
notable progress. However, these methods treat the reconciliation step as a
fixed and hard post-processing step, leading to a trade-off between accuracy
and coherency. In this paper, we propose a new approach for probabilistic
forecast reconciliation. Unlike existing approaches, our proposed approach
fuses the prediction step and reconciliation step into a deep learning
framework, making the reconciliation step more flexible and soft by introducing
the Kullback-Leibler divergence regularization term into the loss function. The
approach is evaluated using three hierarchical time series datasets, which
shows the advantages of our approach over other probabilistic forecast
reconciliation methods.
