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$LEAD\_BIAS^{\tau} = {1\over{N}}\sum\limits_{i=1}^N (\hat{x}_i^{\tau} - x_i^{\tau})$
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Lead time accuracy in standard deviation LEAD_SD
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LEAD$LEAD\_SD^{\tau} = \sqrt{{1\over{N -1}}\sum\limits_{i=1}^N (\hat{x}_i^{\tau} - x_i^{\tau})^2}$
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Lead time accuracy in Mean Square Error LEAD_MSE
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LEAD$LEAD\_MSE^{\tau} = {1\over{N}}\sum\limits_{i=1}^N (\hat{x}_i^{\tau} - x_i^{\tau})^2^2$
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Lead time accuracy in Mean absolute error LEAD_MAE
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LEAD$LEAD\_MAE^{\tau} = {1\over{N}}\sum\limits_{i=1}^N \abs{\hat{x}_i^{\tau} - x_i^{\tau}}
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Lead time accuracy in Root mean square error LEAD_RMSE
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$LEAD\_RMSE^{\tau} = \sqrt{LEAD\_RMSE^MSE^{\tau}} = \sqrt{{1\over{N}}\sum\limits_{i=1}^N (\hat{x}_i^{\tau} - x_i^{\tau})^2}
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where 
is the lead time accuracy at time 
, N is the number of forecasts considered, 
is the reference value at time and 
is the estimated value at time .
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