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306.696  Articles
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The article aims at presenting some of the techniques and methods used for the detection of chaos in time series. The cases study uses the time series of the BETFI index evolution during 2006 and 2009. The nature of the attractor is studied the delay and ... see more

Statistical analysis of data sets is a necessary component of any medical research. Modern methods of mathematical statistics and statistical application suites provide extensive capabilities for analysis of random values. However, when a data set is repr... see more

Nonlinear time series analysis of data related to the radioactivity profile of a hot spring area can explore the dynamics of the geothermal activities along with other different nonlinear features of the Earth system. However, not much work in this field ... see more

The actual problem of modeling of the multivariate nonstationary time series of economic dynamics is being researched for the purpose of analysis, forecasting and decision-making in financial markets.The proposed approach to the modeling of time series is... see more

The Earth's magnetic field varies over a wide range of characteristic times, say, from years to centuries, and more. In order to detect some nonlinear features of the geomagnetic field evolution we first apply a nonlinear spectral technique, i.e. bispectr... see more

This work proposes hybrid models combining time-series models (using linear functions) and artificial intelligence (using a nonlinear function) that can be used to provide monthly mean wind speed predictions for the Brazilian northeast region. These might... see more

In order to detect the multi-component signal from the noise and chaos, a method based on the differential nonlinear mode decomposition (DNMD) is proposed in this paper. This new analysis approach applies the differential to the original signal. Then the ... see more

We present a methodology to determine the nonlinear relationship between an observed dynamic pressure time series at a fixed point below the surface and the surface elevation. This method extends the formalism of previous studies where the analytical rela... see more

This paper proposes a new computational intelligence model for predicting univariate time series, called EPAK, and a complex prediction model for stock market index synthesizing all the sector index predictions using EPAK as a kernel. The EPAK model uses ... see more

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