In this paper we continue our development of new methods for the analysis of broad band time series by deriving quantities which are able to indicate deterministic dependence of an element in one time series on elements in other time series. These methods are very broadly applicable and are particularly well suited to the study of continuous time series, in which the value of the function may depend on derivatives of the function itself, or on other quantities. We apply our methods to a number of mathematical examples including the Lorentz equation, the Henon-Heiles equations, the forced Brusselator and the Mackey-Glass equation. We show that our methods are very successful at indicating deterministic dependencies in these systems, even if ...
Telling a cause from its effect using observed time series data is a major challenge in natural and ...
The classical analysis of stationary time series is based on the study of autocovariances and spectr...
How can fluctuations in one-dimensional time series data be characterized and how can detected effec...
We present a new method for analyzing time series which is designed to extract inherent deterministi...
The traditional tools of data analysis; correlation functions, Fourier transforms, and linear regres...
We derive a normalized version of the indicators of Savit and Green, and prove that these normalized...
The author suggests a heuristic method for detecting the dependence of random time series that can b...
Time series occur in many fields of biology, physics, chemistry, engineering. Much work has been rec...
International audienceIn the past few years, a certain number of authors have proposed analysis meth...
We propose new estimation methods for time series models, possibly noncausal and/or noninvertible, u...
We propose two methods to measure all (linear and nonlinear) statistical dependences in a stationary...
Researchers typically analyze time-series-cross-section data with a binary dependent variable (BTSC...
This paper provides a general methodology for testing for dependence in time series data, with parti...
This thesis is concerned with high-dimensional time series in the context of long-range dependence a...
Mit Hilfe der Quantils Spektralanalyse können die dynamischen Abhängigkeitsstrukturen von Zeitreihen...
Telling a cause from its effect using observed time series data is a major challenge in natural and ...
The classical analysis of stationary time series is based on the study of autocovariances and spectr...
How can fluctuations in one-dimensional time series data be characterized and how can detected effec...
We present a new method for analyzing time series which is designed to extract inherent deterministi...
The traditional tools of data analysis; correlation functions, Fourier transforms, and linear regres...
We derive a normalized version of the indicators of Savit and Green, and prove that these normalized...
The author suggests a heuristic method for detecting the dependence of random time series that can b...
Time series occur in many fields of biology, physics, chemistry, engineering. Much work has been rec...
International audienceIn the past few years, a certain number of authors have proposed analysis meth...
We propose new estimation methods for time series models, possibly noncausal and/or noninvertible, u...
We propose two methods to measure all (linear and nonlinear) statistical dependences in a stationary...
Researchers typically analyze time-series-cross-section data with a binary dependent variable (BTSC...
This paper provides a general methodology for testing for dependence in time series data, with parti...
This thesis is concerned with high-dimensional time series in the context of long-range dependence a...
Mit Hilfe der Quantils Spektralanalyse können die dynamischen Abhängigkeitsstrukturen von Zeitreihen...
Telling a cause from its effect using observed time series data is a major challenge in natural and ...
The classical analysis of stationary time series is based on the study of autocovariances and spectr...
How can fluctuations in one-dimensional time series data be characterized and how can detected effec...