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Linearly immutable continuously time series modelled bivariate stochastic processes with vector values: Distinguishing features

Abstract

Certain observations are assumed to be missed while studying finite continual extended Fourier transformations of time series with precisely stable (i+j) vector values. This is assumed to be the case. This is because the procedure requires studying extended finite Fourier transforms in a standardized manner. The goal is to get as close to an exact interpretation of the results as possible with the data at hand. The results will be put to use in decision-making, which is why this is being done. As a result of this new data, the continuously Fourier transformation will take a starring role in the findings. Asymptotic moments are currently receiving a lot of consideration from researchers all over the world. Case studies on the topic of electrical energy will be used to test our theoretical concepts.

Neural Networks and ApplicationsStatistical Mechanics and EntropyComplex Systems and Time Series AnalysisBivariate analysisFourier seriesFourier transformSeries (stratigraphy)Transformation (genetics)Interpretation (philosophy)Applied mathematicsEnergy (signal processing)MathematicsComputer science
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Linearly immutable continuously time series modelled bivariate stochastic processes with vector values: Distinguishing features · Scinovex