Sentence examples for mean squared forecasting from inspiring English sources

Also, Xiaolong (2004) derived and measured a forecasting procedure that minimizes the mean squared forecasting error for the specified demand process.

An analysis of the forecasting models using mean absolute deviations and mean squared errors indicated that simple mathematical forecasting techniques provided better predictions of meal demand than did the naı̈Adive method for all sites.

Post-processing reduces both the biases and the root mean squared error of the raw forecasts significantly.

The criterion for quantifying the performance of wind power forecasting is normalized root mean squared error (NRMSE).

Using daily data for exchange rates between the US dollar and the Mauritian rupee, they compared the mean absolute error (MAE) and root mean squared error (RMSE) of the models based on forecasting estimates.

The performance indexes used in the table are: The mean squared error (MSE) measuring the average error of the forecasting results: begin{aligned} {mathrm{RMSE}}= sqrt{frac{1}{N}sum ^{N}_{i=1}(t_i - hat{t}_i)^2}, ; ; i=1, ldots, N, end{aligned} (22) where (t_i) is the observed traffic value, (hat{t}_i) is its forecasted value and N is the size of the test set.

Coefficient of determination, (relative) root mean squared error and Nash-Sutcliffe efficiency were used to evaluate forecasts, the model selection was based on an out-of-sample validation on rolling basis.

The mean squared error (MSE: a quantity used to measure how close forecasts or predictions are to the eventual outcomes), time (TBM: time to build model), and model stability (STD: ST Deviation obtained from ten sub-samples) were also measured in the experiments.

Case study shows that the ultra-short-term wind forecasting accuracy is further improved in terms of normalized root mean squared error (NRMSE).

The ultra-short-term wind power forecasting accuracy is further improved by using error correction in terms of normalized root mean squared error (NRMSE).

The Pearson correlation coefficient (R), mean squared error skill score (MSESS), and Bias were used to evaluate the skill of downscaled deterministic forecasts.