[PDF] Top 20 Noise reduction in nonlinear time series analysis
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Noise reduction in nonlinear time series analysis
... So far we have only discussed a limited set of optimisation algorithms as methods for tackling the noise reduction problem but there are obviously other candidates that might be worth considering. Here we ... See full document
152
Dimensional reduction of nonlinear time delay systems
... stability analysis of a nonlinear dynamical system at a particular equilibrium point, there is a pair of complex-conjugate eigenvalues crossing the imaginary axis with a nonzero velocity, and while the ... See full document
18
Characterizing the continuously acquired cardiovascular time series during hemodialysis, using median hybrid filter preprocessing noise reduction
... Nonsustained runs of trend linearity occur; however, they appear inconsistent in terms of length, direction, and turning point. Changes in SBP trajectory and level appear rapidly but are neither captured nor conveyed ... See full document
6
Chaos in magnetospheric radio emissions
... temporal time series of radio emissions from planets, the Sun and pul- sars often exhibit quasi-periodic, intermittent or chaotic tem- poral patterns (Zhuravlev and Popov, 90; Isliker and Benz, 1994; Chian ... See full document
7
Nonlinear time series analysis of annual temperatures concerning the global Earth climate
... the series is generated by a nonlinear process, given that all the linear structure has been ...both series of shuffled and surrogate residuals the ... See full document
26
Nonlinear dynamical analysis of GNSS data: quantification, precursors and synchronisation
... any time or spatial series analysis is a data model which enables one to characterise the statis- tical measures (mean, standard deviation, autocorrel- ation function, power spectrum and so on) of ... See full document
35
Nonlinear time series: semiparametric and nonparametric methods
... tests for a parametric form versus a nonparametric form, tests for a semi- parametric (partially linear or single–index) form against a nonparamet- ric form, and tests for the significance of a subset of the ... See full document
245
Search for Additive Nonlinear Time Series Causal Models
... from time series data have been developed using multiple conditional independence tests, but no such procedures are available for non-linear ...non-linear time series structures, which we call ... See full document
25
Volterra series truncation and reduction in the frequency domain for weakly nonlinear system
... practical nonlinear continuous time system identification problems, when only input-output observations are available, there will be a balance between a physically meaningful identification, where the ... See full document
11
Dimensionality Reduction for Time Series Decoding and Forecasting Problems
... To eliminate the linear dependence and reduce the dimensionality of the input space, principal components analysis (PCA) is widely used. The main disadvantage of the PCA method is its insensitivity to the ... See full document
11
Development of new analytical techniques for chaotic time series
... carried out by Caton [125]. Until 1970, EEG interpretation was principally heuristic and qualitative [126, 127, 128], i.e. basically relying on the subjective interpretation of recordings. 1985 is a crucial year for the ... See full document
140
Estimation and testing of persistence in nonlinear and cyclical time series
... these series should have the same degree of long-run persistence, we find substantial differences in the point estimates of their memory ...particular time span, the estimates of their memory parameters are ... See full document
253
Bayesian inference for nonlinear structural time series models
... Table 5 also shows the variance of each filter’s loglikelihood estimates, evaluated by taking 75 repeated loglikelihood estimates at the true value of θ. These results are broadly consistent with the asymptotic ... See full document
30
Image Noise Reduction Using Linear and Nonlinear Filtering Techniques
... Abstract— Noise is always presents in digital images during image capturing, coding, transmission, and processing ...of noise in the resulting ...transmission. Noise is very difficult to remove it ... See full document
6
Concepts and tools for nonlinear time series modelling
... Tweedie, Random Coefficient Autoregressive Processes: a Markov Chain Analysis of Stationarity and Finiteness of Moments, Journal of Time Series Analysis, 6 1985 1–14.. Findley, The overf[r] ... See full document
64
Software Reliability Modeling in Fuzzy Environment
... reliability nonlinear modeling based on time series Time series analysis theory is a method of describing statistics character of dynamics data, which can set up time ... See full document
8
White Noise Analysis: A Measure of Time Series Model Adequacy
... on time lags and that the second order stationarity plus an assumption of normality are suffi- cient to produce strict stationarity (see also, [4] ...a time series is said to be stationary, if it has ... See full document
15
Fractional cointegration analysis of nonlinear time series with long memory
... We have presented an application of theoretical techniques developed in Chap ter 3 to a multivariate setting. In the context of a multivariate factor model, where both the factors and idiosyncratic errors are driven by ... See full document
223
Wavelet analysis for non-stationary, nonlinear time series
... traditional wavelet analysis (Maraun et al., 2007; Schulte et al., 2015; Schulte, 2016). In the case of simultaneously test- ing multiple hypotheses, the number of false positive results can be reduced by ... See full document
11
Essays in high dimensional nonlinear time series analysis
... component analysis PCA (Pearson, 1901; Eckart and Young, 1936), factor analysis FA (Spearman, 1904), canonical cor- relations analysis CCA (Hotelling, 1936), multidimensional scaling (Torgerson, ... See full document
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