часовий ряд

Analysis of the debris flow formation factors which cause the long-term activity of debris flows is made. The methodology of the debris flows prediction subject to meteorological, hydrological, seismic, heliophysical factors is proposed. The regularities of long-term seasonality of these factors by using autocorrelation and spectral analysis are revealed. The integral rate of probability of debris flow intensification is calculated. The time series of this integral rate is extrapolated and the following peak of debris flows activation is predicted.

Presented a model of carrier personnel from experimental results. Model of the filed two components – a trend equation and the distribution of deviations from trend data. Trend equation is analytic function – with mixed polynomial degree. Rejection levels approximated by Rayleigh distribution law. Both models provide a system for objective assessments identifying personnel.

In this paper the radial-basis neural network was used for software failures prediction. The influence of activation function of the RBF neural net on the learning efficiency and software failures prediction is studied. It is shown that the optimal activation function is Inverse Multiquadric with 10 neurons in the input layer and 30 neurons in the hidden one (square of Pearson correlation coefficient is 0.997 and mean deviation is 14.4).

The recurrence plots tool origination and foundation process is considered. Construction method of recurrence plots based on time series is described. Recurrent plots dependence on the input parameters is analyzed and shown.