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Nonlinear Time Series Analysis
1998This tutorial review presents an overview of the achievements and some present research activities in the field of state space based methods for nonlinear time-series analysis. In particular, questions of state space reconstruction, of modelling and prediction, of filtering and noise reduction, of detecting non-linearities in time series, and ...
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Nonlinear Time Series Analysis
2018The recent development of nonlinear time series analysis is primarily due to the efforts to overcome the limitations of linear models such as autoregressive moving-average (ARMA) models of Box and Jenkins (1976) in real applications. Two examples of such limitations are the non-ability to model sudden outbursts and the restriction to symmetry in the ...
Ruey S. Tay, Rong Chen
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Surrogate for nonlinear time series analysis
Physical Review E, 2001We present a surrogate for use in nonlinear time series analysis. This surrogate algorithm has significant advantages over the most commonly used surrogates, in that it provides a more robust statistical test by producing an entire population of surrogates that are consistent with the null hypothesis.
K T, Dolan, M L, Spano
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Nonlinear Time Series Forecasting
1992Having gone through Chapters 8 and 9, you may justifiably get the feeling that subjective judgment may be required in order to determine the existence of an underlying chaotic attractor dictating the dynamics of an observable. Limitations of the algorithms, data requirements, and other problems discussed in these chapters often make the interpretation ...
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DETECTING NONLINEARITIES IN STATIONARY TIME SERIES
International Journal of Bifurcation and Chaos, 1993In this review we survey methods for detecting nonlinearities in stationary time series. These methods are based on the estimation of so-called correlation integrals. These correlation integrals provide a way of analyzing time series and reveal aspects which are often complementary to the information one obtains from power spectra and autocorrelations.
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Nonlinear Properties of Cardiovascular Time Series
1999Nonlinear dynamics are certainly involved in tlie generation and control of cardiovascular signais. The paper presents a methodological and clinical review of nonlinear dynainic analysis in heart rate variability signals (HRV) obtained from normal subjects and pathological cases.
SIGNORINI, MARIA GABRIELLA +1 more
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Modern Nonlinear Time Series Methods
2016Advances in computational power, scientific concepts, and data measurements have led to the development of numerous nonlinear methods to study complex systems normally encountered in various scientific fields. These nonlinear methods often have very different conceptual bases and levels of sophistication and have been found suitable for studying many ...
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