We present a novel method for the parameter oriented analysis of mutual correlation between independent time series or between equivalent structures such as ordered data sets.

The proposed method is based on the sliding window technique, defines a new type of correlation measure and can be applied to time series from all domains of science and technology, experimental or simulated. A specific parameter that can characterize the time series is computed for each window and a cross correlation analysis is carried out on the set of values obtained for the time series under investigation.

We apply this method to the study of some currency daily exchange rates from the point of view of the Hurst exponent and the intermittency parameter. Interesting correlation relationships are revealed and a tentative crisis prediction is presented.

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We propose a new method for the study of the correlations of fluctuations in time-series, using the multifractal analysis. The investigation is performed on the partial data sets obtained from the original series, for positive and negative fluctuations.

The method offers the possibility of identifying correlations either in each sub-series or between them. The application of the method to financial timeseries allows estimation on the predictability of evolution. As case study, the exchange rates Leu-USD and Leu-EUR are considered.

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We present a simple and straightforward method for computing Hurst exponent of fractal, self-similar time-series by direct use of the defining relationship.

It is based on a set of series derived from the original time series constructed considering log-returns for increasing delays. The method can be applied to selfsimilar time series of any kind, irrespective of their origin.

In the present study we only consider financial time series and the computed Hurst exponent is used for estimation of market volatility. As case study, the exchange rates of ROL, CZK, GBP and SEK versus US Dollar and Euro are considered.

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The magnitude coherence function (MCF) and the magnitude coherence index (MCI) of the exchange rates ROL/USD-ROL/EUR are computed using the estimator of the smoothed periodograms of time length of one quarter. The significance of the result is tested against both the shuffling procedure of the series and the overlapping windows of the estimator.

Significant coherences were found at all frequencies with little increase toward the short term cycles of the order of several days. This is considered to be the particular effect of the short run psychological market force which synchronously drives the exchange rates additional to the economic market forces.

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In this work we investigate the influence of white Gaussian noise on the fluctuations in the plasma of a symmetrical discharge using multifractal detrended fluctuation analysis.

We observe that in the range of noise intensity used in our study, the multifractality strength is increased by the noise, at all values of the inter-anode voltage, both for original and filtered time-series.This is interpreted as a new positive influence of noise because this effect can be understood as an increasing in the predictability on the dynamics in a time-series.

A constructive influence of noise can appear only for fluctuations with underlying chaotic dynamics. The shuffling analysis demonstrates that the multifractality is purely a consequence of the correlations of the fluctuations. The noise influence is also observed in the change of the position of the maximum in the singularity spectra.

The multifractal detrended cross correlation between light intensity and current intensity demonstrates that the fluctuations in both parameters are generated by the same physical processes though they are very different in nature: one is a local parameter and the other is a global one.

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