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Weather forecast skill is often presented in the form of seasonal geographical maps. Forecasting skill for single-value forecasts (i.e., time series of a scalar quantity) is commonly represented in terms of metrics such as correlation, root mean squared error, mean absolute error, relative mean absolute error, bias, and the Brier score, among ...
A skill score for a given underlying score is an offset and (negatively-) scaled variant of the underlying score such that a skill score value of zero means that the score for the predictions is merely as good as that of a set of baseline or reference or default predictions, while a skill score value of one (100%) represents the best possible ...
In decision theory, a scoring rule [1] provides evaluation metrics for probabilistic predictions or forecasts. While "regular" loss functions (such as mean squared error) assign a goodness-of-fit score to a predicted value and an observed value, scoring rules assign such a score to a predicted probability distribution and an observed value.
The second example suggests a good method of normalizing a forecast before applying any skill measure. Most weather situations will cycle, since the Earth is forced by a highly regular energy source. A numerical weather model must accurately model both the seasonal cycle and (if finely resolved enough) the diurnal cycle.
Common metrics of forecast error, such as mean absolute error, geometric mean absolute error, and mean squared error, have shortcomings related to dependence on scale of data and/or handling zeros and negative values within the data. Hyndman's MASE metric resolves these and can be used under any forecast generation method. [6]
It is a measure used to evaluate the performance of regression or forecasting models. It is a variant of MAPE in which the mean absolute percent errors is treated as a weighted arithmetic mean. Most commonly the absolute percent errors are weighted by the actuals (e.g. in case of sales forecasting, errors are weighted by sales volume). [3]
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The Nash–Sutcliffe model efficiency coefficient (NSE) is used to assess the predictive skill of hydrological models. It is defined as: = ...