Testing Stationarity with Time-Frequency Surrogates

International audienceA method is proposed for testing stationarity in an operational sense, i.e., by both including explicitly an observation scale in the definition and elaborating a stationarized reference so as to reject the null hypothesis of stationarity with a controlled level of statistical significance. While the approach is classically based on comparing local vs. global features in the time-frequency plane, the test operates with a family of stationarized surrogates whose analysis allows for a characterization of the null hypothesis. The general principle of the method is outlined, practical issues related to its actual implementation are discussed and a typical example is provided for illustrating the approach and supporting its effectiveness

Testing Stationarity with Surrogates: A Time-Frequency Approach

International audienceAn operational framework is developed for testing stationarity relatively to an observation scale, in both stochastic and deterministic contexts. The proposed method is based on a comparison between global and local time-frequency features. The originality is to make use of a family of stationary surrogates for defining the null hypothesis of stationarity and to base on them two different statistical tests. The first one makes use of suitably chosen distances between local and global spectra, whereas the second one is implemented as a one-class classifier, the time-frequency features extracted from the surrogates being interpreted as a learning set for stationarity. The principle of the method and of its two variations is presented, and some results are shown on typical models of signals that can be thought of as stationary or nonstationary, depending on the observation scale used

Statistical hypothesis testing with time-frequency surrogates to check signal stationarity

International audienceAn operational framework is developed for testing stationarity relatively to an observation scale. The proposed method makes use of a family of stationary surrogates for defining the null hypothesis of stationarity. As a further contribution to the field, we demonstrate the strict-sense stationarity of surrogate signals and we exploit this property to derive the asymptotic distributions of their spectrogram and power spectral density. A statistical hypothesis testing framework is then proposed to check signal stationarity. Finally, some results are shown on a typical model of signals that can be thought of as stationary or nonstationary, depending on the observation scale used

Surrogate time series

Before we apply nonlinear techniques, for example those inspired by chaos theory, to dynamical phenomena occurring in nature, it is necessary to first ask if the use of such advanced techniques is justified "by the data". While many processes in nature seem very unlikely a priori to be linear, the possible nonlinear nature might not be evident in specific aspects of their dynamics. The method of surrogate data has become a very popular tool to address such a question. However, while it was meant to provide a statistically rigorous, foolproof framework, some limitations and caveats have shown up in its practical use. In this paper, recent efforts to understand the caveats, avoid the pitfalls, and to overcome some of the limitations, are reviewed and augmented by new material. In particular, we will discuss specific as well as more general approaches to constrained randomisation, providing a full range of examples. New algorithms will be introduced for unevenly sampled and multivariate data and for surrogate spike trains. The main limitation, which lies in the interpretability of the test results, will be illustrated through instructive case studies. We will also discuss some implementational aspects of the realisation of these methods in the TISEAN (http://www.mpipks-dresden.mpg.de/~tisean) software package.Comment: 28 pages, 23 figures, software at http://www.mpipks-dresden.mpg.de/~tisea

A simple method for detecting chaos in nature

Chaos, or exponential sensitivity to small perturbations, appears everywhere in nature. Moreover, chaos is predicted to play diverse functional roles in living systems. A method for detecting chaos from empirical measurements should therefore be a key component of the biologist's toolkit. But, classic chaos-detection tools are highly sensitive to measurement noise and break down for common edge cases, making it difficult to detect chaos in domains, like biology, where measurements are noisy. However, newer tools promise to overcome these limitations. Here, we combine several such tools into an automated processing pipeline, and show that our pipeline can detect the presence (or absence) of chaos in noisy recordings, even for difficult edge cases. As a first-pass application of our pipeline, we show that heart rate variability is not chaotic as some have proposed, and instead reflects a stochastic process in both health and disease. Our tool is easy-to-use and freely available

Time-Frequency Surrogates for Nonstationary Signal Analysis

International audienceThe purpose of the present communication is, after a brief outline of the use of simple surrogates as introduced so far for stationarity tests, to deal with constructions of surrogates in ''time-frequency domains''. Indeed, for transient detection or cross-correlations analysis, one need to construct directly ''surrogate time-frequency distributions'', as opposed to distributions of surrogate time series, and keeping the `geometrical' structure in the plane of the quadratic distribution

Revisiting and testing stationarity

6 pages, 4 figures, 10 references. To be presented at the 2008 Euro American Workshop on Information Optics will be held during June 1 - 5, 2008 at Les Tresoms in Annecy, France.The concept of stationarity is revisited from an operational perspective that explicitly takes into account the observation scale. A general framework is described for testing such a relative stationarity via the introduction of stationarized surrogate data