Corrupted bifractal features in finite uncorrelated power-law distributed data

Multifractal Detrended Fluctuation Analysis stands out as one of the most reliable methods for unveiling multifractal properties, specially when real-world time series are under analysis. However, little is known about how several aspects, like artefacts during data acquisition process, affect its results. In this work we have numerically investigated performance applied to synthetic finite uncorrelated following a power-law distribution in presence additive noise, and periodic randomly-placed outliers. We found that, on hand, spurious multifractality observed result finiteness, while noise leads an underestimation exponents hq q<0 even low levels. On other randomly-located outliers corrupted inverse around q=0. Moreover, corrupts entire spectrum, way proportional their density. As application, properties intervals between successive aircraft landings at three major European airports investigated.