A statistical data screening procedure

A statistical method for automatic quality control of measurement data with distributions close to Gaussian is presented. For each data point a prediction is made, based on the mean, variance and point-to-point correlation of the time series. The predicted value is compared with the actual value and if the difference between the two is 'large' then that data point is either marked as an outlier or replaced by the forecast value. Four different prediction methods are tested and, using the best one of these, the method is tested using artificial and real turbulence data.