Detecting the relationships among multivariate time series using reduced auto-regressive modeling.

An information theoretic reduction of auto-regressive modeling called the Reduced Auto-Regressive (RAR) modeling is applied to several multivariate time series as a method to detect the relationships among the components in the time series. The results are compared with the results of the transfer entropy, one of the common techniques for detecting causal relationships. These common techniques are pairwise by definition and could be inappropriate in detecting the relationships in highly complicated dynamical systems.

Long-range correlation properties of stationary linear models with mixed periodicities.

We consider the problem of (stationary and linear) source systems which generate time series data with long-range correlations. We use the discrete Fourier transform (DFT) and build stationary linear models using artificial time series data exhibiting a 1/f spectrum, where the models can include only terms that contribute significantly to the model as assessed by information criteria. The result is that the optimal (best) model is only composed of mixed periodicities [that is, the model does not include all (continuous) periodicities] and the time series data generated by the model exhibit a clear 1/f spectrum in a wide frequency range.

Constructing networks from a dynamical system perspective for multivariate nonlinear time series.

We describe a method for constructing networks for multivariate nonlinear time series. We approach the interaction between the various scalar time series from a deterministic dynamical system perspective and provide a generic and algorithmic test for whether the interaction between two measured time series is statistically significant. The method can be applied even when the data exhibit no obvious qualitative similarity: a situation in which the naive method utilizing the cross correlation function directly cannot correctly identify connectivity.

From phase space to frequency domain: a time-frequency analysis for chaotic time series.

Time-frequency analysis is performed for chaotic flow with a power spectrum estimator based on the phase-space neighborhood. The relation between the reference phase point and its nearest neighbors is demonstrated. The nearest neighbors, representing the state recurrences in the phase space reconstructed by time delay embedding, actually cover data segments with similar wave forms and thus possess redundant information, but recur with no obvious temporal regularity.

Detecting temporal and spatial correlations in pseudoperiodic time series.

Recently there has been much attention devoted to exploring the complicated possibly chaotic dynamics in pseudoperiodic time series. Two methods [Zhang, Phys. Rev.

Testing for correlation structures in short-term variabilities with long-term trends of multivariate time series.

We describe a method for identifying correlation structures in irregular fluctuations (short-term variabilities) of multivariate time series, even if they exhibit long-term trends. This method is based on the previously proposed small shuffle surrogate method. The null hypothesis addressed by this method is that there is no short-term correlation structure among data or that the irregular fluctuations are independent.

Testing for nonlinearity in irregular fluctuations with long-term trends.

We describe a method for investigating nonlinearity in irregular fluctuations (short-term variability) of time series even if the data exhibit long-term trends (periodicities). Such situations are theoretically incompatible with the assumption of previously proposed methods. The null hypothesis addressed by our algorithm is that irregular fluctuations are generated by a stationary linear system.

Degeneracy of time series models: the best model is not always the correct model.

There are a number of good techniques for finding, in some sense, the best model of a deterministic system given a time series of observations. We examine a problem called model degeneracy, which has the consequence that even when a perfect model of a system exists, one does not find it using the best techniques currently available. The problem is illustrated using global polynomial models and the theory of Grobner bases.

Small-shuffle surrogate data: testing for dynamics in fluctuating data with trends.

We describe a method for identifying dynamics in irregular time series (short term variability). The method we propose focuses attention on the flow of information in the data. We can apply the method even for irregular fluctuations which exhibit long term trends (periodicities): situations in which previously proposed surrogate methods would give erroneous results.

Testing for nonlinearity in time series without the Fourier transform.

A method to test for nonlinearity in time series, without the need to apply the Fourier transform, is proposed. This method therefore avoids the drawbacks of previously proposed surrogate techniques associated with the estimation of the signal's power spectrum. The test addressed by this algorithm is that the data are generated by a stationary linear system.

Surrogate test to distinguish between chaotic and pseudoperiodic time series.

In this paper a different algorithm is proposed to produce surrogates for pseudoperiodic time series. By imposing a few constraints on the noise components of pseudoperiodic data sets, we devise an effective method to generate surrogates. Unlike other algorithms, this method properly copes with pseudoperiodic orbits contaminated with linear colored observational noise.