Abstract
We exploit ideas of nonlinear dynamics in a non-deterministic dynamical setting. Our object of study is the observed riverflow time series of the Portuguese Paiva river whose water is used for public supply. The Takens delay embedding of the daily riverflow time series revealed an intermittent dynamical behaviour due to precipitation occurrence. The laminar phase occurs in the absence of rainfall. The nearest neighbour method of prediction revealed good predictability in the laminar regime but we warn that this method is misleading in the presence of rain. The correlation integral curve analysis, Singular Value Decomposition and the Nearest Neighbour Method indicate that the laminar regime of flow is in a small neighbourhood of a one-dimensional affine subspace in the phase space. The Nearest Neighbour method attested also that in the laminar phase and for a data set of 53 years the information of the current runoff is by far the most relevant information to predict future runoff. However the information of the past two runoffs is important to correct non-linear effects of the riverflow as the MSE and MRE criteria results show. The results point out that the Nearest Neighbours method fails when used in the irregular phase because it does not predict precipitation occurrence. © 2007, Birkhäuser Verlag Basel/Switzerland.

Abstract
We consider the. re-scaled PSI20 daily index positive returns r(t)(alpha) and negative returns (-r(t))(alpha) called, after normalization, the. positive and negative fluctuations, respectively. We use the Kolmogorov-Smirnov statistical test as a method to find the values of alpha that optimize the data collapse of the histogram of the. fluctuations with the truncated Bramwell-Holdsworth-Pinton (BHP) probability density function (pdf) fBHP and the truncated generalized log-normal pdf fLN that best approximates the truncated BHP pdf. The optimal parameters we found are alpha(+)(BHP) = 0.48, alpha(-)(BHP) = 0.46, alpha(+)(LN) = 0.50 and alpha(-)(LN) = 0.49. Using the optimal alpha's we compute the analytical approximations of the pdf of the normalized positive and negative PSI20 index daily returns r(t). Since the BHP probability density function appears in several other dissimilar phenomena, our result reveals a universal feature of the stock exchange markets.

Abstract
We analyze the famous Wolf's sunspot numbers. Surprisingly, we discovered that the distribution of the sunspot number fluctuations for both the ascending and descending phases is close to the universal nonparametric Bramwell-Holdsworth-Pinton (BHP) distribution. Since the BHP probability density function appears in several other physical phenomena, our result reveals a universal feature of the Wolf's sunspot numbers.

Abstract
We analyze the constituents stocks of the well known Standard & Poor's 100 index (S&P100) that are traded in the NYSE and NASDAQ markets. We observe the data collapse of the histogram of the S&P100 index fluctuations to the universal non-parametric Bramwell-Holdsworth-Pinton (BHP) distribution. Since the BHP probability density function appears in several other dissimilar phenomena, our result reveals an universal feature of the stock exchange markets.

Abstract
We present a simplified cycle model, using the available data, for the monthly sunspot number random variables {X-t}(t=1)(133), where 133 is taken as the mean duration of the Schwabe's cycle. We present a fit for the mean and standard deviation of X-t. In the descending and ascending phases, we analyse the probability histogram of the monthly sunspot number fluctuations.

Abstract
We compute the analytic expression of the probability distributions F(AEX,+) and F(AEX,-) of the normalized positive and negative AEX (Netherlands) index daily returns r(t). Furthermore, we define the alpha re-scaled AEX daily index positive returns r(t)(alpha) and negative returns (-r(t))(alpha), which we call, after normalization, the alpha positive fluctuations and alpha negative fluctuations. We use the Kolmogorov-Smirnov statistical test as a method to find the values of alpha that optimize the data collapse of the histogram of the alpha fluctuations with the Bramwell-Holdsworth-Pinton (BHP) probability density function. The optimal parameters that we found are alpha(+) = 0.46 and alpha(-) = 0.43. Since the BHP probability density function appears in several other dissimilar phenomena, our result reveals a universal feature of stock exchange markets.