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We investigate the presence of multifractal residual background effect for monofractal signals which appears due to the finite length of the signals and (or) due to the constant long memory the signals reveal. This phenomenon is investigated numerically within the multifractal detrended fluctuation analysis (MF-DFA) for artificially generated time series. Next, the analytical formulas enabling to describe the multifractal content in such signals are provided. Final results are shown in the frequently used generalized Hurst exponent h(q) multifractal scenario as a function of time series length L and the autocorrelation scaling exponent value γ. The obtained results may be significant in any practical application of multifractality, including financial data analysis, because the "true" multifractal effect should be clearly separated from the so called "multifractal noise" resulting from the finite data length. Examples from finance in this context are given. The provided formulas may help to decide whether one deals with the signal of real multifractal origin or not and make further step in analysis of the so called spurious or corrupted multifractality discussed in literature.

We study quantitatively the level of false multifractal signal one may encounter while analyzing multifractal phenomena in time series within multifractal detrended fluctuation analysis (MF-DFA). The investigated effect appears as a result of finite length of used data series and is additionally amplified by the long-term memory the data eventually may contain. We provide the detailed quantitative description of such apparent multifractal background signal as a threshold in spread of generalized Hurst exponent values $\Delta h$ or a threshold in the width of multifractal spectrum $\Delta \alpha$ below which multifractal properties of the system are only apparent, i.e. do not exist, despite $\Delta\alpha\neq0$ or $\Delta h\neq 0$. We find this effect quite important for shorter or persistent series and we argue it is linear with respect to autocorrelation exponent $\gamma$. Its strength decays according to power law with respect to the length of time series. The influence of basic linear and nonlinear transformations applied to initial data in finite time series with various level of long memory is also investigated. This provides additional set of semi-analytical results. The obtained formulas are significant in any interdisciplinary application of multifractality, including physics, financial data analysis or physiology, because they allow to separate the 'true' multifractal phenomena from the apparent (artificial) multifractal effects. They should be a helpful tool of the first choice to decide whether we do in particular case with the signal with real multiscaling properties or not.Comment: 36 pages, 41 figure

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We focus on the importance of q moments range used within the multifractal detrended fluctuation analysis (MFDFA), to calculate the generalized Hurst exponent spread and multifractal properties of signals. Different orders of detrending polynomials are also discussed. In particular, we analyze quantitatively the corrections to the spread of the generalized Hurst exponent profile . They allow to extend the previously found formulas describing the level of artificial multiscaling in finite signals for large q to an arbitrary narrower range of q moments used in MFDFA technique in distinct applications. Examples of the implementation of proposed corrections to real multifractal financial data are provided at the end.