Probabilistic Forecasting with Coherent Aggregation

no code implementations • 19 Jul 2023 • Geoffrey Négiar, Ruijun Ma, O. Nangba Meetei, Mengfei Cao, Michael W. Mahoney

Our model uses a convolutional neural network to produce parameters for the factors, their loadings and base-level distributions; it produces samples which can be differentiated with respect to the model's parameters; and it can therefore optimize for any sample-based loss function, including the Continuous Ranked Probability Score and quantile losses.

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