Thomas Stemler

Time lagged ordinal partition networks for capturing dynamics of continuous dynamical systems

We investigate a generalised version of the recently proposed ordinal partition time series to network transformation algorithm. First, we introduce a fixed time lag for the elements of each partition that is selected using techniques from traditional time delay embedding. The resulting partitions define regions in the embedding phase space that are mapped to nodes in the network space. Edges are allocated between nodes based on temporal succession thus creating a Markov chain representation of the time series. We then apply this new transformation algorithm to time series generated by the Rössler system and find that periodic dynamics translate to ring structures whereas chaotic time series translate to band or tube-like structures-thereby indicating that our algorithm generates networks whose structure is sensitive to system dynamics. Furthermore, we demonstrate that simple network measures including the mean out degree and variance of out degrees can track changes in the dynamical behaviour in a manner comparable to the largest Lyapunov exponent. We also apply the same analysis to experimental time series generated by a diode resonator circuit and show that the network size, mean shortest path length, and network diameter are highly sensitive to the interior crisis captured in this particular data set.

What exactly are the properties of scale-free and other networks?

The concept of scale-free networks has been widely applied across natural and physical sciences. Many claims are made about the properties of these networks, even though the concept of scale-free is often vaguely defined. We present tools and procedures to analyse the statistical properties of networks defined by arbitrary degree distributions and other constraints. Doing so reveals the highly likely properties, and some unrecognised richness, of scale-free networks, and casts doubt on some previously claimed properties being due to a scale-free characteristic.

Forecasting: it is not about statistics, it is about dynamics

In 1963, the mathematician and meteorologist Edward Lorenz published a paper (Lorenz 1963 J. Atmos. Sci. 20, 130–141) that changed the way scientists think about the prediction of geophysical systems, by introducing the ideas of chaos, attractors, sensitivity to initial conditions and the limitations to forecasting nonlinear systems. Three years earlier, the mathematician and engineer Rudolf Kalman had published a paper (Kalman 1960 Trans. ASME Ser. D, J. Basic Eng. 82, 35–45) that changed the way engineers thought about prediction of electronic and mechanical systems. Ironically, in recent years, geophysicists have become increasingly interested in Kalman filters, whereas engineers have become increasingly interested in chaos. It is argued that more often than not the tracking and forecasting of nonlinear systems has more to do with the nonlinear dynamics that Lorenz considered than it has to do with statistics that Kalman considered. A position with which both Lorenz and Kalman would appear to agree.

Regenerating time series from ordinal networks

Recently proposed ordinal networks not only afford novel methods of nonlinear time series analysis but also constitute stochastic approximations of the deterministic flow time series from which the network models are constructed. In this paper, we construct ordinal networks from discrete sampled continuous chaotic time series and then regenerate new time series by taking random walks on the ordinal network. We then investigate the extent to which the dynamics of the original time series are encoded in the ordinal networks and retained through the process of regenerating new time series by using several distinct quantitative approaches. First, we use recurrence quantification analysis on traditional recurrence plots and order recurrence plots to compare the temporal structure of the original time series with random walk surrogate time series. Second, we estimate the largest Lyapunov exponent from the original time series and investigate the extent to which this invariant measure can be estimated from the surrogate time series. Finally, estimates of correlation dimension are computed to compare the topological properties of the original and surrogate time series dynamics. Our findings show that ordinal networks constructed from univariate time series data constitute stochastic models which approximate important dynamical properties of the original systems.

Stochastic modelling of intermittency

Recently, methods have been developed to model low-dimensional chaotic systems in terms of stochastic differential equations. We tested such methods in an electronic circuit experiment. We aimed to obtain reliable drift and diffusion coefficients even without a pronounced time-scale separation of the chaotic dynamics. By comparing the analytical solutions of the corresponding Fokker–Planck equation with experimental data, we show here that crisis-induced intermittency can be described in terms of a stochastic model which is dominated by state-space-dependent diffusion. Further on, we demonstrate and discuss some limits of these modelling approaches using numerical simulations. This enables us to state a criterion that can be used to decide whether a stochastic model will capture the essential features of a given time series.

Tracking a single pigeon using a shadowing filter algorithm

Abstract Miniature GPS devices now allow for measurement of the movement of animals in real time and provide high‐ quality and high‐resolution data. While these new data sets are a great improvement, one still encounters some measurement errors as well as device failures. Moreover, these devices only measure position and require further reconstruction techniques to extract the full dynamical state space with the velocity and acceleration. Direct differentiation of position is generally not adequate. We report on the successful implementation of a shadowing filter algorithm that (1) minimizes measurement errors and (2) reconstructs at the same time the full phase‐space from a position recording of a flying pigeon. This filter is based on a very simple assumption that the pigeons dynamics are Newtonian. We explore not only how to choose the filters parameters but also demonstrate its improvements over other techniques and give minimum data requirements. In contrast to competing filters, the shadowing filters approach has not been widely implemented for practical problems. This article addresses these practicalities and provides a prototype for such application.

Counting forbidden patterns in irregularly sampled time series. II. Reliability in the presence of highly irregular sampling

We are motivated by real-world data that exhibit severe sampling irregularities such as geological or paleoclimate measurements. Counting forbidden patterns has been shown to be a powerful tool towards the detection of determinism in noisy time series. They constitute a set of ordinal symbolic patterns that cannot be realised in time series generated by deterministic systems. The reliability of the estimator of the relative count of forbidden patterns from irregularly sampled data has been explored in two recent studies. In this paper, we explore highly irregular sampling frequency schemes. Using numerically generated data, we examine the reliability of the estimator when the sampling period has been drawn from exponential, Pareto and Gamma distributions of varying skewness. Our investigations demonstrate that some statistical properties of the sampling distribution are useful heuristics for assessing the estimators reliability. We find that sampling in the presence of large chronological gaps can still yield relatively accurate estimates as long as the time series contains sufficiently many densely sampled areas. Furthermore, we show that the reliability of the estimator of forbidden patterns is poor when there is a high number of sampling intervals, which are larger than a typical correlation time of the underlying system.

Counting forbidden patterns in irregularly sampled time series. I. The effects of under-sampling, random depletion, and timing jitter.

It has been established that the count of ordinal patterns, which do not occur in a time series, called forbidden patterns, is an effective measure for the detection of determinism in noisy data. A very recent study has shown that this measure is also partially robust against the effects of irregular sampling. In this paper, we extend said research with an emphasis on exploring the parameter space for the methods sole parameter-the length of the ordinal patterns-and find that the measure is more robust to under-sampling and irregular sampling than previously reported. Using numerically generated data from the Lorenz system and the hyper-chaotic Rössler system, we investigate the reliability of the relative proportion of ordinal patterns in periodic and chaotic time series for various degrees of under-sampling, random depletion of data, and timing jitter. Discussion and interpretation of results focus on determining the limitations of the measure with respect to optimal parameter selection, the quantity of data available, the sampling period, and the Lyapunov and de-correlation times of the system.

Spatiotemporal stochastic resonance in an array of Schmitt triggers

We report on stochastic resonance in an array of four unidirectionally coupled Schmitt triggers driven by global noise and a spatiotemporal modulation. By introducing phase shifts in the drives of these bistable electronic triggers we were able to control the amplification of the periodic input signal, which is a characteristic of stochastic resonance. For phase shifts allowing for periodic boundary conditions, we found array-enhanced stochastic resonance. Moreover, evidence for spatiotemporal stochastic multiresonance was obtained, where the array exhibits more than one maximum of amplification. We attribute these phenomena to a competition between the external drive and the coupling of an array element with its neighbor.

From Flocs to Flocks

In this chapter we present the different ways in which mathematicians think about group formation and group behaviour. We provide an overview of the frameworks that have been borrowed from Physics and discuss their appropriateness and limitations when applied to groups such as a flock of birds. With a focus at the the level of local behaviour and global results, we explore the origin of flocking models that paved the way for studying some of the most fascinating phenomena and hottest topics of the last few decades self organisation, emergence and coherent collective motion. We then outline the standard approach for model formulation and analysis with two key stages in mind: grouping and flocking - usually treated quite separately in the literature. That is, how do individual motivations drive aggregation of particles to groups, and then how do these groups generate coordinated collective motion. Finally we hint at future work that aims to amalgamate these two stages with a single model.