Learning with the Nash-Sutcliffe loss

Ali Nemati6 days ago26 sec read15 views

Researchers introduced Nash-Sutcliffe loss as a negatively oriented counterpart to the widely used Nash-Sutcliffe efficiency (NSE) for evaluating forecasts across multiple time series, providing a decision-theoretic foundation and proving its consistency. This development enhances forecast evaluation by allowing for more accurate modeling of multi-dimensional data with varying stochastic properties, emphasizing the benefits of global models over local ones in large datasets.

Read the full article at arXiv stat.ML

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