  In this paper, we study long-term correlations and multifractal properties
elaborated from time series of three-phase current signals coming from an
industrial electric arc furnace plant. Implicit sinusoidal trends are suitably
detected by considering the scaling of the fluctuation functions. Time series
are then filtered via a Fourier-based analysis, removing hence such strong
periodicities. In the filtered time series we detected long-term, positive
correlations. The presence of positive correlations is in agreement with the
typical V--I characteristic (hysteresis) of the electric arc furnace, providing
thus a sound physical justification for the memory effects found in the current
time series. The multifractal signature is strong enough in the filtered time
series to be effectively classified as multifractal.
