Financial time series analysis : Chaos and neurodynamics approach

This work aims at combining the Chaos theory postulates and Artificial Neural Networks classification and predictive capability, in the field of financial time series prediction. Chaos theory, provides valuable qualitative and quantitative tools to decide on the predictability of a chaotic system. Quantitative measurements based on Chaos theory, are used, to decide a-priori whether a time series, or a portion of a time series is predictable, while Chaos theory based qualitative tools are used to provide further observations and analysis on the predictability, in cases where measurements provide negative answers. Phase space reconstruction is achieved by time delay embedding resulting in multiple embedded vectors. The cognitive approach suggested, is inspired by the capability of some chartists to predict the direction of an index by looking at the price time series. Thus, in this work, the calculation of the embedding dimension and the separation, in Takens‘ embedding theorem for phase space reconstruction, is not limited to False Nearest Neighbor, Differential Entropy or other specific method, rather, this work is interested in all embedding dimensions and separations that are regarded as different ways of looking at a time series by different chartists, based on their expectations. Prior to the prediction, the embedded vectors of the phase space are classified with Fuzzy-ART, then, for each class a back propagation Neural Network is trained to predict the last element of each vector, whereas all previous elements of a vector are used as features.

Detecção de falhas em sistemas incertos com atraso no sinal de controle

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Explosive synchronization enhanced by time-delayed coupling

This paper deals with the emergence of explosive synchronization in scale-free networks by considering the Kuramoto model of coupled phase oscillators. The natural frequencies of oscillators are assumed to be correlated with their degrees, and a time delay is included in the system. This assumption allows enhancing the explosive transition to reach a synchronous state. We provide an analytical treatment developed in a star graph, which reproduces results obtained in scale-free networks. Our findings have important implications in understanding the synchronization of complex networks since the time delay is present in most real-world complex systems due to the finite speed of the signal transmission over a distance.

Closed-loop stable model predictive control of integrating systems with dead time

Model predictive control (MPC) applications in the process industry usually deal with process systems that show time delays (dead times) between the system inputs and outputs. Also, in many industrial applications of MPC, integrating outputs resulting from liquid level control or recycle streams need to be considered as controlled outputs. Conventional MPC packages can be applied to time-delay systems but stability of the closed loop system will depend on the tuning parameters of the controller and cannot be guaranteed even in the nominal case. In this work, a state space model based on the analytical step response model is extended to the case of integrating time systems with time delays. This model is applied to the development of two versions of a nominally stable MPC, which is designed to the practical scenario in which one has targets for some of the inputs and/or outputs that may be unreachable and zone control (or interval tracking) for the remaining outputs. The controller is tested through simulation of a multivariable industrial reactor system. (C) 2012 Elsevier Ltd. All rights reserved.

Evolutionary demography of long-lived monocarpic perennials: a time-lagged integral projection model

1. The evolution of flowering strategies (when and at what size to flower) in monocarpic perennials is determined by balancing current reproduction with expected future reproduction, and these are largely determined by size-specific patterns of growth and survival. However, because of the difficulty in following long-lived individuals throughout their lives, this theory has largely been tested using short-lived species (< 5 years). 2. Here, we tested this theory using the long-lived monocarpic perennial Campanula thyrsoides which can live up to 16 years. We used a novel approach that combined permanent plot and herb chronology data from a 3-year field study to parameterize and validate integral projection models (IPMs). 3. Similar to other monocarpic species, the rosette leaves of C. thyrsoides wither over winter and so size cannot be measured in the year of flowering. We therefore extended the existing IPM framework to incorporate an additional time delay that arises because flowering demography must be predicted from rosette size in the year before flowering. 4. We found that all main demographic functions (growth, survival probability, flowering probability and fecundity) were strongly size-dependent and there was a pronounced threshold size of flowering. There was good agreement between the predicted distribution of flowering ages obtained from the IPMs and that estimated in the field. Mostly, there was good agreement between the IPM predictions and the direct quantitative field measurements regarding the demographic parameters lambda, R-0 and T. We therefore conclude that the model captures the main demographic features of the field populations. 5. Elasticity analysis indicated that changes in the survival and growth function had the largest effect (c. 80%) on lambda and this was considerably larger than in short-lived monocarps. We found only weak selection pressure operating on the observed flowering strategy which was close to the predicted evolutionary stable strategy. 6. Synthesis. The extended IPM accurately described the demography of a long-lived monocarpic perennial using data collected over a relatively short period. We could show that the evolution of flowering strategies in short- and long-lived monocarps seem to follow the same general rules but with a longevity-related emphasis on survival over fecundity.

Time domain passivity control for delayed teleoperation

La telepesencia combina diferentes modalidades sensoriales, incluyendo, entre otras, la visual y la del tacto, para producir una sensación de presencia remota en el operador. Un elemento clave en la implementación de sistemas de telepresencia para permitir una telemanipulación del entorno remoto es el retorno de fuerza. Durante una telemanipulación, la energía mecánica es transferida entre el operador humano y el entorno remoto. En general, la energía es una propiedad de los objetos físicos, fundamental en su mutual interacción. En esta interacción, la energía se puede transmitir entre los objetos, puede cambiar de forma pero no puede crearse ni destruirse. En esta tesis, se aplica este principio fundamental para derivar un nuevo método de control bilateral que permite el diseño de sistemas de teleoperación estables para cualquier arquitectura concebible. El razonamiento parte del hecho de que la energía mecánica insertada por el operador humano en el sistema debe transferirse hacia el entorno remoto y viceversa. Tal como se verá, el uso de la energía como variable de control permite un tratamiento más general del sistema que el control convencional basado en variables específicas del sistema. Mediante el concepto de Red de Potencia de Retardo Temporal (RPRT), el problema de definir los flujos de energía en un sistema de teleoperación es solucionado con independencia de la arquitectura de comunicación. Como se verá, los retardos temporales son la principal causa de generación de energía virtual. Este hecho se observa con retardos a partir de 1 milisegundo. Esta energía virtual es añadida al sistema de forma intrínseca y representa la causa principal de inestabilidad. Se demuestra que las RPRTs son transportadoras de la energía deseada intercambiada entre maestro y esclavo pero a la vez generadoras de energía virtual debido al retardo temporal. Una vez estas redes son identificadas, el método de Control de Pasividad en el Dominio Temporal para RPRTs se propone como mecanismo de control para asegurar la pasividad del sistema, y as__ la estabilidad. El método se basa en el simple hecho de que esta energía virtual debido al retardo debe transformarse en disipación. As__ el sistema se aproxima al sistema deseado, donde solo la energía insertada desde un extremo es transferida hacia el otro. El sistema resultante presenta dos cualidades: por un lado la estabilidad del sistema queda garantizada con independencia de la arquitectura del sistema y del canal de comunicación; por el otro, el rendimiento es maximizado en términos de fidelidad de transmisión energética. Los métodos propuestos se sustentan con sistemas experimentales con diferentes arquitecturas de control y retardos entre 2 y 900 ms. La tesis concluye con un experimento que incluye una comunicación espacial basada en el satélite geoestacionario ASTRA. ABSTRACT Telepresence combines different sensorial modalities, including vision and touch, to produce a feeling of being present in a remote location. The key element to successfully implement a telepresence system and thus to allow telemanipulation of a remote environment is force feedback. In a telemanipulation, mechanical energy must convey from the human operator to the manipulated object found in the remote environment. In general, energy is a property of all physical objects, fundamental to their mutual interactions in which the energy can be transferred among the objects and can change form but cannot be created or destroyed. In this thesis, we exploit this fundamental principle to derive a novel bilateral control mechanism that allows designing stable teleoperation systems with any conceivable communication architecture. The rationale starts from the fact that the mechanical energy injected by a human operator into the system must be conveyed to the remote environment and Vice Versa. As will be seen, setting energy as the control variable allows a more general treatment of the controlled system in contrast to the more conventional control of specific systems variables. Through the Time Delay Power Network (TDPN) concept, the issue of defining the energy flows involved in a teleoperation system is solved with independence of the communication architecture. In particular, communication time delays are found to be a source of virtual energy. This fact is observed with delays starting from 1 millisecond. Since this energy is added, the resulting teleoperation system can be non-passive and thus become unstable. The Time Delay Power Networks are found to be carriers of the desired exchanged energy but also generators of virtual energy due to the time delay. Once these networks are identified, the Time Domain Passivity Control approach for TDPNs is proposed as a control mechanism to ensure system passivity and therefore, system stability. The proposed method is based on the simple fact that this intrinsically added energy due to the communication must be transformed into dissipation. Then the system becomes closer to the ambitioned one, where only the energy injected from one end of the system is conveyed to the other one. The resulting system presents two benefits: On one hand, system stability is guaranteed through passivity independently from the chosen control architecture and communication channel; on the other, performance is maximized in terms of energy transfer faithfulness. The proposed methods are sustained with a set of experimental implementations using different control architectures and communication delays ranging from 2 to 900 milliseconds. An experiment that includes a communication Space link based on the geostationary satellite ASTRA concludes this thesis.

A Modified Positive Velocity and Position Feedback scheme with delay compensation for improved nanopositioning performance

Acknowledgments This paper was sponsored by the Spanish FPU12/00984 Program (Ministerio de Educacion, Cultura y Deporte). It was also sponsored by the Spanish Government Research Program with the Project DPI2012-37062-CO2-01 (Ministerio de Economia y Competitividad) and by the European Social Fund.

Bathorhodopsin structure in the room-temperature rhodopsin photosequence: picosecond time-resolved coherent anti-Stokes Raman scattering.

Structural changes in the retinal chromophore during the formation of the bathorhodopsin intermediate (bathoRT) in the room-temperature rhodopsin (RhRT) photosequence (i.e., vision) are examined using picosecond time-resolved coherent anti-Stokes Raman scattering. Specifically, the retinal structure assignable to bathoRT following 8-ps excitation of RhRT is measured via vibrational Raman spectroscopy at a 200-ps time delay where the only intermediate present is bathoRT. Significant differences are observed between the C=C stretching frequencies of the retinal chromophore at low temperature where bathorhodopsin is stabilized and at room temperature where bathorhodopsin is a transient species in the RhRT photosequence. These vibrational data are discussed in terms of the formation of bathoRT, an important step in the energy storage/transduction mechanism of RhRT.

Stochastic delay differential equations for genetic regulatory networks

Time delay is an important aspect in the modelling of genetic regulation due to slow biochemical reactions such as gene transcription and translation, and protein diffusion between the cytosol and nucleus. In this paper we introduce a general mathematical formalism via stochastic delay differential equations for describing time delays in genetic regulatory networks. Based on recent developments with the delay stochastic simulation algorithm, the delay chemical masterequation and the delay reaction rate equation are developed for describing biological reactions with time delay, which leads to stochastic delay differential equations derived from the Langevin approach. Two simple genetic regulatory networks are used to study the impact of' intrinsic noise on the system dynamics where there are delays. (c) 2006 Elsevier B.V. All rights reserved.

Variable time-scale information processing

With the extensive use of pulse modulation methods in telecommunications, much work has been done in the search for a better utilisation of the transmission channel.The present research is an extension of these investigations. A new modulation method, 'Variable Time-Scale Information Processing', (VTSIP), is proposed.The basic principles of this system have been established, and the main advantages and disadvantages investigated. With the proposed system, comparison circuits detect the instants at which the input signal voltage crosses predetermined amplitude levels.The time intervals between these occurrences are measured digitally and the results are temporarily stored, before being transmitted.After reception, an inverse process enables the original signal to be reconstituted.The advantage of this system is that the irregularities in the rate of information contained in the input signal are smoothed out before transmission, allowing the use of a smaller transmission bandwidth. A disadvantage of the system is the time delay necessarily introduced by the storage process.Another disadvantage is a type of distortion caused by the finite store capacity.A simulation of the system has been made using a standard speech signal, to make some assessment of this distortion. It is concluded that the new system should be an improvement on existing pulse transmission systems, allowing the use of a smaller transmission bandwidth, but introducing a time delay.

Soliton propagation through a disordered system:statistics of the transmission delay

We have studied the soliton propagation through a segment containing random pointlike scatterers. In the limit of small concentration of scatterers when the mean distance between the scatterers is larger than the soliton width, a method has been developed for obtaining the statistical characteristics of the soliton transmission through the segment. The method is applicable for any classical particle traversing through a disordered segment with the given velocity transformation after each act of scattering. In the case of weak scattering and relatively short disordered segment the transmission time delay of a fast soliton is mostly determined by the shifts of the soliton center after each act of scattering. For sufficiently long segments the main contribution to the delay is due to the shifts of the amplitude and velocity of a fast soliton after each scatterer. Corresponding crossover lengths for both cases of light and heavy solitons have been obtained. We have also calculated the exact probability density function of the soliton transmission time delay for a sufficiently long segment. In the case of weak identical scatterers the latter is a universal function which depends on a sole parameter—the mean number of scatterers in a segment.

Approximating differentiable relationships between delay embedded dynamical systems with radical basis functions

This thesis is about the study of relationships between experimental dynamical systems. The basic approach is to fit radial basis function maps between time delay embeddings of manifolds. We have shown that under certain conditions these maps are generically diffeomorphisms, and can be analysed to determine whether or not the manifolds in question are diffeomorphically related to each other. If not, a study of the distribution of errors may provide information about the lack of equivalence between the two. The method has applications wherever two or more sensors are used to measure a single system, or where a single sensor can respond on more than one time scale: their respective time series can be tested to determine whether or not they are coupled, and to what degree. One application which we have explored is the determination of a minimum embedding dimension for dynamical system reconstruction. In this special case the diffeomorphism in question is closely related to the predictor for the time series itself. Linear transformations of delay embedded manifolds can also be shown to have nonlinear inverses under the right conditions, and we have used radial basis functions to approximate these inverse maps in a variety of contexts. This method is particularly useful when the linear transformation corresponds to the delay embedding of a finite impulse response filtered time series. One application of fitting an inverse to this linear map is the detection of periodic orbits in chaotic attractors, using suitably tuned filters. This method has also been used to separate signals with known bandwidths from deterministic noise, by tuning a filter to stop the signal and then recovering the chaos with the nonlinear inverse. The method may have applications to the cancellation of noise generated by mechanical or electrical systems. In the course of this research a sophisticated piece of software has been developed. The program allows the construction of a hierarchy of delay embeddings from scalar and multi-valued time series. The embedded objects can be analysed graphically, and radial basis function maps can be fitted between them asynchronously, in parallel, on a multi-processor machine. In addition to a graphical user interface, the program can be driven by a batch mode command language, incorporating the concept of parallel and sequential instruction groups and enabling complex sequences of experiments to be performed in parallel in a resource-efficient manner.

Hidden state models for time series

Amongst all the objectives in the study of time series, uncovering the dynamic law of its generation is probably the most important. When the underlying dynamics are not available, time series modelling consists of developing a model which best explains a sequence of observations. In this thesis, we consider hidden space models for analysing and describing time series. We first provide an introduction to the principal concepts of hidden state models and draw an analogy between hidden Markov models and state space models. Central ideas such as hidden state inference or parameter estimation are reviewed in detail. A key part of multivariate time series analysis is identifying the delay between different variables. We present a novel approach for time delay estimating in a non-stationary environment. The technique makes use of hidden Markov models and we demonstrate its application for estimating a crucial parameter in the oil industry. We then focus on hybrid models that we call dynamical local models. These models combine and generalise hidden Markov models and state space models. Probabilistic inference is unfortunately computationally intractable and we show how to make use of variational techniques for approximating the posterior distribution over the hidden state variables. Experimental simulations on synthetic and real-world data demonstrate the application of dynamical local models for segmenting a time series into regimes and providing predictive distributions.

Noise-induced standing waves in oscillatory systems with time-delayed feedback

In oscillatory reaction-diffusion systems, time-delay feedback can lead to the instability of uniform oscillations with respect to formation of standing waves. Here, we investigate how the presence of additive, Gaussian white noise can induce the appearance of standing waves. Combining analytical solutions of the model with spatio-temporal simulations, we find that noise can promote standing waves in regimes where the deterministic uniform oscillatory modes are stabilized. As the deterministic phase boundary is approached, the spatio-temporal correlations become stronger, such that even small noise can induce standing waves in this parameter regime. With larger noise strengths, standing waves could be induced at finite distances from the (deterministic) phase boundary. The overall dynamics is defined through the interplay of noisy forcing with the inherent reaction-diffusion dynamics.

Improving the predictability of time series by chaotic parameter estimation

Limited literature regarding parameter estimation of dynamic systems has been identified as the central-most reason for not having parametric bounds in chaotic time series. However, literature suggests that a chaotic system displays a sensitive dependence on initial conditions, and our study reveals that the behavior of chaotic system: is also sensitive to changes in parameter values. Therefore, parameter estimation technique could make it possible to establish parametric bounds on a nonlinear dynamic system underlying a given time series, which in turn can improve predictability. By extracting the relationship between parametric bounds and predictability, we implemented chaos-based models for improving prediction in time series. ^ This study describes work done to establish bounds on a set of unknown parameters. Our research results reveal that by establishing parametric bounds, it is possible to improve the predictability of any time series, although the dynamics or the mathematical model of that series is not known apriori. In our attempt to improve the predictability of various time series, we have established the bounds for a set of unknown parameters. These are: (i) the embedding dimension to unfold a set of observation in the phase space, (ii) the time delay to use for a series, (iii) the number of neighborhood points to use for avoiding detection of false neighborhood and, (iv) the local polynomial to build numerical interpolation functions from one region to another. Using these bounds, we are able to get better predictability in chaotic time series than previously reported. In addition, the developments of this dissertation can establish a theoretical framework to investigate predictability in time series from the system-dynamics point of view. ^ In closing, our procedure significantly reduces the computer resource usage, as the search method is refined and efficient. Finally, the uniqueness of our method lies in its ability to extract chaotic dynamics inherent in non-linear time series by observing its values. ^