Uncertainty evaluation in time-dependent measurements

Abstract

Data quality plays a strong role in modelling and is fundamental to ensure that obtained analyses and results are reliable. Data uncertainty is related to the measurement’s imperfections that will cause an error in the obtained numerical value. In this way, data quality could be measured through uncertainty. Although there are well-established methods for uncertainty evaluation in independent measurements, there is a gap of methods in the case of time-dependent measurements. This study aims to propose a general methodology to evaluate the uncertainty of time-dependent measurements. Fitting an appropriate model to the measurements and then resampling and refitting a model, the uncertainty estimation of each data point is possible. The approach was validated using measurements obtained from typical time series models and compared with theoretical values. Further, real data from a water system is also used to illustrate the method’ capacity for uncertainty estimation of single time measurements.

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