Delay Vector Variance (DVV) method, Surrogate Data, Hypothesis Testing, Test Statistics, Signal Nonlinearity and Uncertainty, Wind Modelling

Introduction

The Delay vector variance (DVV) method uses predictability of the signal in phase space to characterize the time series. Using the surrogate data methodology, so called DVV plots and DVV scatter diagrams can be generated using the DVV method, as a test statistic, to examine the determinism/stochastisity and linearity/nonlinearity within a signal simultaneously. In DVV scatter diagram, the target variance values of the original signal is plotted against the averaged variance values, calculated over a number of iAAFT surrogates. As a result, for linear signals, the scatter diagram coincides with the bisector line and conversely for nonlinear signals, the scatter diagram deviates from bisector lineas shown in the attached document. The DVV method has been successfully applied to analyse the nature of biometric signals (EEG and fMRI).

The DVV Toolbox

A DVV toolbox for MATLAB is provided, which can be downloaded as a zip archive. The code is provided under the GNU General Public License (GPL).

Acknowledgements: Temujin Gautama, Marc Van Hulle, Mo Chen, Naveed Ur Rehman

References

    Legend: MATLAB code, PDF files, Supplements.

    You can download from here some Wind Data used in some of the simulations in the work below. These are 2D (complex) data, for three different wind regimes of 'low', 'medium', and 'high' dynamics.

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