The envelope-based cyclic periodogram

Cyclostationary analysis has proven effective in identifying signal components for diagnostic purposes. A key descriptor in this framework is the cyclic power spectrum, traditionally estimated by the averaged cyclic periodogram and the smoothed cyclic periodogram. A lengthy debate about the best estimator finally found a solution in a cornerstone work by Antoni, who proposed a unified form for the two families, thus allowing a detailed statistical study of their properties. Since then, the focus of cyclostationary research has shifted towards algorithms, in terms of computational efficiency and simplicity of implementation. Traditional algorithms have proven computationally inefficient and the sophisticated "cyclostationary" definition of these estimators slowed their spread in the industry. The only attempt to increase the computational efficiency of cyclostationary estimators is represented by the cyclic modulation spectrum. This indicator exploits the relationship between cyclostationarity and envelope analysis. The link with envelope analysis allows a leap in computational efficiency and provides a "way in" for the understanding by industrial engineers. However, the new estimator lies outside the unified form described above and an unbiased version of the indicator has not been proposed. This paper will therefore extend the analysis of envelope-based estimators of the cyclic spectrum, proposing a new approach to include them in the unified form of cyclostationary estimators. This will enable the definition of a new envelope-based algorithm and the detailed analysis of the properties of the cyclic modulation spectrum. The computational efficiency of envelope-based algorithms will be also discussed quantitatively for the first time in comparison with the averaged cyclic periodogram. Finally, the algorithms will be validated with numerical and experimental examples.

Stochastic modelling of financial time series with memory and multifractal scaling

Financial processes may possess long memory and their probability densities may display heavy tails. Many models have been developed to deal with this tail behaviour, which reflects the jumps in the sample paths. On the other hand, the presence of long memory, which contradicts the efficient market hypothesis, is still an issue for further debates. These difficulties present challenges with the problems of memory detection and modelling the co-presence of long memory and heavy tails. This PhD project aims to respond to these challenges. The first part aims to detect memory in a large number of financial time series on stock prices and exchange rates using their scaling properties. Since financial time series often exhibit stochastic trends, a common form of nonstationarity, strong trends in the data can lead to false detection of memory. We will take advantage of a technique known as multifractal detrended fluctuation analysis (MF-DFA) that can systematically eliminate trends of different orders. This method is based on the identification of scaling of the q-th-order moments and is a generalisation of the standard detrended fluctuation analysis (DFA) which uses only the second moment; that is, q = 2. We also consider the rescaled range R/S analysis and the periodogram method to detect memory in financial time series and compare their results with the MF-DFA. An interesting finding is that short memory is detected for stock prices of the American Stock Exchange (AMEX) and long memory is found present in the time series of two exchange rates, namely the French franc and the Deutsche mark. Electricity price series of the five states of Australia are also found to possess long memory. For these electricity price series, heavy tails are also pronounced in their probability densities. The second part of the thesis develops models to represent short-memory and longmemory financial processes as detected in Part I. These models take the form of continuous-time AR(∞) -type equations whose kernel is the Laplace transform of a finite Borel measure. By imposing appropriate conditions on this measure, short memory or long memory in the dynamics of the solution will result. A specific form of the models, which has a good MA(∞) -type representation, is presented for the short memory case. Parameter estimation of this type of models is performed via least squares, and the models are applied to the stock prices in the AMEX, which have been established in Part I to possess short memory. By selecting the kernel in the continuous-time AR(∞) -type equations to have the form of Riemann-Liouville fractional derivative, we obtain a fractional stochastic differential equation driven by Brownian motion. This type of equations is used to represent financial processes with long memory, whose dynamics is described by the fractional derivative in the equation. These models are estimated via quasi-likelihood, namely via a continuoustime version of the Gauss-Whittle method. The models are applied to the exchange rates and the electricity prices of Part I with the aim of confirming their possible long-range dependence established by MF-DFA. The third part of the thesis provides an application of the results established in Parts I and II to characterise and classify financial markets. We will pay attention to the New York Stock Exchange (NYSE), the American Stock Exchange (AMEX), the NASDAQ Stock Exchange (NASDAQ) and the Toronto Stock Exchange (TSX). The parameters from MF-DFA and those of the short-memory AR(∞) -type models will be employed in this classification. We propose the Fisher discriminant algorithm to find a classifier in the two and three-dimensional spaces of data sets and then provide cross-validation to verify discriminant accuracies. This classification is useful for understanding and predicting the behaviour of different processes within the same market. The fourth part of the thesis investigates the heavy-tailed behaviour of financial processes which may also possess long memory. We consider fractional stochastic differential equations driven by stable noise to model financial processes such as electricity prices. The long memory of electricity prices is represented by a fractional derivative, while the stable noise input models their non-Gaussianity via the tails of their probability density. A method using the empirical densities and MF-DFA will be provided to estimate all the parameters of the model and simulate sample paths of the equation. The method is then applied to analyse daily spot prices for five states of Australia. Comparison with the results obtained from the R/S analysis, periodogram method and MF-DFA are provided. The results from fractional SDEs agree with those from MF-DFA, which are based on multifractal scaling, while those from the periodograms, which are based on the second order, seem to underestimate the long memory dynamics of the process. This highlights the need and usefulness of fractal methods in modelling non-Gaussian financial processes with long memory.

On the computation and interpretation of auto- and cross-trispectra

This paper discusses the principal domains of auto- and cross-trispectra. It is shown that the cumulant and moment based trispectra are identical except on certain planes in trifrequency space. If these planes are avoided, their principal domains can be derived by considering the regions of symmetry of the fourth order spectral moment. The fourth order averaged periodogram will then serve as an estimate for both cumulant and moment trispectra. Statistics of estimates of normalised trispectra or tricoherence are also discussed.

Practical Algorithms For Mean Velocity Estimation In Pulse Doppler Weather Radars Using A Small Number Of Samples

Doppler weather radars with fast scanning rates must estimate spectral moments based on a small number of echo samples. This paper concerns the estimation of mean Doppler velocity in a coherent radar using a short complex time series. Specific results are presented based on 16 samples. A wide range of signal-to-noise ratios are considered, and attention is given to ease of implementation. It is shown that FFT estimators fare poorly in low SNR and/or high spectrum-width situations. Several variants of a vector pulse-pair processor are postulated and an algorithm is developed for the resolution of phase angle ambiguity. This processor is found to be better than conventional processors at very low SNR values. A feasible approximation to the maximum entropy estimator is derived as well as a technique utilizing the maximization of the periodogram. It is found that a vector pulse-pair processor operating with four lags for clear air observation and a single lag (pulse-pair mode) for storm observation may be a good way to estimate Doppler velocities over the entire gamut of weather phenomena.

A robust initialization scheme for faster convergence of the dichotomous search algorithm for single frequency estimation

The estimation of the frequency of a sinusoidal signal is a well researched problem. In this work we propose an initialization scheme to the popular dichotomous search of the periodogram peak algorithm(DSPA) that is used to estimate the frequency of a sinusoid in white gaussian noise. Our initialization is computationally low cost and gives the same performance as the DSPA, while reducing the number of iterations needed for the fine search stage. We show that our algorithm remains stable as we reduce the number of iterations in the fine search stage. We also compare the performance of our modification to a previous modification of the DSPA and show that we enhance the performance of the algorithm with our initialization technique.

Comparison of pattern detection methods in microarray time series of the segmentation clock.

While genome-wide gene expression data are generated at an increasing rate, the repertoire of approaches for pattern discovery in these data is still limited. Identifying subtle patterns of interest in large amounts of data (tens of thousands of profiles) associated with a certain level of noise remains a challenge. A microarray time series was recently generated to study the transcriptional program of the mouse segmentation clock, a biological oscillator associated with the periodic formation of the segments of the body axis. A method related to Fourier analysis, the Lomb-Scargle periodogram, was used to detect periodic profiles in the dataset, leading to the identification of a novel set of cyclic genes associated with the segmentation clock. Here, we applied to the same microarray time series dataset four distinct mathematical methods to identify significant patterns in gene expression profiles. These methods are called: Phase consistency, Address reduction, Cyclohedron test and Stable persistence, and are based on different conceptual frameworks that are either hypothesis- or data-driven. Some of the methods, unlike Fourier transforms, are not dependent on the assumption of periodicity of the pattern of interest. Remarkably, these methods identified blindly the expression profiles of known cyclic genes as the most significant patterns in the dataset. Many candidate genes predicted by more than one approach appeared to be true positive cyclic genes and will be of particular interest for future research. In addition, these methods predicted novel candidate cyclic genes that were consistent with previous biological knowledge and experimental validation in mouse embryos. Our results demonstrate the utility of these novel pattern detection strategies, notably for detection of periodic profiles, and suggest that combining several distinct mathematical approaches to analyze microarray datasets is a valuable strategy for identifying genes that exhibit novel, interesting transcriptional patterns.

SuperWASP observations of long timescale photometric variations in cataclysmic variables

Aims: We investigated whether the predictions and results of Stanishev et al. (2002, A&A, 394, 625) concerning a possible relationship between eclipse depths in PX And and its retrograde disc precession phase, could be confirmed in long term observations made by SuperWASP. In addition, two further CVs (DQ Her and V795 Her) in the same SuperWASP data set were investigated to see whether evidence of superhump periods and disc precession periods were present and what other, if any, long term periods could be detected. Methods: Long term photometry of PX And, V795 Her and DQ Her was carried out and Lomb-Scargle periodogram analysis undertaken on the resulting light curves. For the two eclipsing CVs, PX And and DQ Her, we analysed the potential variations in the depth of the eclipse with cycle number. Results: The results of our period and eclipse analysis on PX And confirm that the negative superhump period is 0.1417 ± 0.0001d. We find no evidence of positive superhumps in our data suggesting that PX And may have been in a low state during our observations. We improve on existing estimates of the disc precession period and find it to be 4.43 ± 0.05d. Our results confirm the predictions of Stanishev et al. (2002). We find that DQ Her does not appear to show a similar variation for we find no evidence of negative superhumps or of a retrograde disc precession. We also find no evidence of positive superhumps or of a prograde disc precession and we attribute the lack of positive superhumps in DQ Her to be due to the high mass ratio of this CV. We do however find evidence for a modulation of the eclipse depth over a period of 100 days which may be linked with solar-type magnetic cycles which give rise to long term photometric variations. The periodogram analysis for V795 Her detected the likely positive superhump period 0.1165d, however, neither the 0.10826d orbital period nor the prograde 1.53d disc precession period were seen. Here though we have found a variation in the periodogram power function at the positive superhump period, over a period of at least 120 days.

A window on exoplanet dynamical histories: Rossiter-McLaughlin observations of WASP-13b and WASP-32b

We present Rossiter-McLaughlin observations of WASP-13b and WASP-32b and determine the sky-projected angle between the normal of the planetary orbit and the stellar rotation axis (λ). WASP-13b and WASP-32b both have prograde orbits and are consistent with alignment with measured sky-projected angles of λ =8°^{+13}_{-12} and λ =-2°^{+17}_{-19}, respectively. Both WASP-13 and WASP-32 have Teff < 6250 K, and therefore, these systems support the general trend that aligned planetary systems are preferentially found orbiting cool host stars. A Lomb-Scargle periodogram analysis was carried out on archival SuperWASP data for both systems. A statistically significant stellar rotation period detection (above 99.9 per cent confidence) was identified for the WASP-32 system with Prot =11.6 ± 1.0 days. This rotation period is in agreement with the predicted stellar rotation period calculated from the stellar radius, R*, and vsin i if a stellar inclination of i* =90° is assumed. With the determined rotation period, the true 3D angle between the stellar rotation axis and the planetary orbit, ψ, was found to be ψ = 11° ± 14°. We conclude with a discussion on the alignment of systems around cool host stars with Teff < 6150 K by calculating the tidal dissipation time-scale. We find that systems with short tidal dissipation time-scales are preferentially aligned and systems with long tidal dissipation time-scales have a broad range of obliquities.

Low Complexity Spectral Analysis of Heart-Rate-Variability through a Wavelet based FFT

In this paper, a low complexity system for spectral analysis of heart rate variability (HRV) is presented. The main idea of the proposed approach is the implementation of the Fast-Lomb periodogram that is a ubiquitous tool in spectral analysis, using a wavelet based Fast Fourier transform. Interestingly we show that the proposed approach enables the classification of processed data into more and less significant based on their contribution to output quality. Based on such a classification a percentage of less-significant data is being pruned leading to a significant reduction of algorithmic complexity with minimal quality degradation. Indeed, our results indicate that the proposed system can achieve up-to 45% reduction in number of computations with only 4.9% average error in the output quality compared to a conventional FFT based HRV system.

Climate spectrum estimation in the presence of timescale errors.

We introduce an algorithm (called REDFITmc2) for spectrum estimation in the presence of timescale errors. It is based on the Lomb-Scargle periodogram for unevenly spaced time series, in combination with the Welch's Overlapped Segment Averaging procedure, bootstrap bias correction and persistence estimation. The timescale errors are modelled parametrically and included in the simulations for determining (1) the upper levels of the spectrum of the red-noise AR(1) alternative and (2) the uncertainty of the frequency of a spectral peak. Application of REDFITmc2 to ice core and stalagmite records of palaeoclimate allowed a more realistic evaluation of spectral peaks than when ignoring this source of uncertainty. The results support qualitatively the intuition that stronger effects on the spectrum estimate (decreased detectability and increased frequency uncertainty) occur for higher frequencies. The surplus information brought by algorithm REDFITmc2 is that those effects are quantified. Regarding timescale construction, not only the fixpoints, dating errors and the functional form of the age-depth model play a role. Also the joint distribution of all time points (serial correlation, stratigraphic order) determines spectrum estimation.

Atmospheric electric field measurements in urban environment and the pollutant aerosol weekly dependence

The weekly dependence of pollutant aerosols in the urban environment of Lisbon (Portugal) is inferred from the records of atmospheric electric field at Portela meteorological station (38°47′N,9°08′W). Measurements were made with a Bendorf electrograph. The data set exists from 1955 to 1990, but due to the contaminating effect of the radioactive fallout during 1960 and 1970s, only the period between 1980 and 1990 is considered here. Using a relative difference method a weekly dependence of the atmospheric electric field is found in these records, which shows an increasing trend between 1980 and 1990. This is consistent with a growth of population in the Lisbon metropolitan area and consequently urban activity, mainly traffic. Complementarily, using a Lomb–Scargle periodogram technique the presence of a daily and weekly cycle is also found. Moreover, to follow the evolution of theses cycles, in the period considered, a simple representation in a colour surface plot representation of the annual periodograms is presented. Further, a noise analysis of the periodograms is made, which validates the results found. Two datasets were considered: all days in the period, and fair-weather days only.

Temporal aggregation and bandwidth selection in estimating long memory

This paper reinterprets results of Ohanissian et al (2003) to show the asymptotic equivalence of temporally aggregating series and using less bandwidth in estimating long memory by Geweke and Porter-Hudak’s (1983) estimator, provided that the same number of periodogram ordinates is used in both cases. This equivalence is in the sense that their joint distribution is asymptotically normal with common mean and variance and unity correlation. Furthermore, I prove that the same applies to the estimator of Robinson (1995). Monte Carlo simulations show that this asymptotic equivalence is a good approximation in finite samples. Moreover, a real example with the daily US Dollar/French Franc exchange rate series is provided.

Um estudo da abundância de lítio, rotação, atividade cromosférica e magnetismo das estrelas análogas e gêmeas solares

The study physical process that control the stellar evolution is strength influenced by several stellar parameters, like as rotational velocity, convective envelope mass deepening, and magnetic field intensity. In this study we analyzed the interconnection of some stellar parameters, as Lithium abundance A(Li), chromospheric activity and magnetic field intensity as well as the variation of these parameters as a function of age, rotational velocity, and the convective envelope mass deepening for a selected sample of solar analogs and twins stars. In particular, we analyzed the convective envelope mass deepening and the dispersion of lithium abundance for these stars. We also studied the evolution of rotation in subgiants stars, because its belong to the following evolutionary stage of solar analogs, and twins stars. For this analyze, we compute evolutionary models with the TGEC code to derive the evolutionary stage, as well as the convective envelope mass deepening, and derive more precisely the stellar mass, and age for this 118 stars. Our Investigation shows a considerable dispersion of lithium abundance for the solar analogs stars. We also realize that this dispersion is not by the convective zone deep, in this way we observed which the scattering of A(Li) can not be explained by classical theories of mixing in the convective zone. In conclusion we have that are necessary extra-mixing process to explain this decrease of Lithium abundance in solar analogs and twins stars. We analyzed the subgiant stars because this are the subsequent evolutionary stage after the solar analogs and twins stars. For this analysis, we compute the rotational period for 30 subgiants stars observed by Co- RoT satellite. For this task we apply two different methods: Lomb-Scargle algorithm, and the Plavchan Periodogram. We apply the TGEC code we compute models with internal distribution of angular momentum to confront the predict results with the models, and the observational results. With this analyze, we showed which solid body rotation models are incompatible with the physical interpretation of observational results. As a result of our study we still concluded that the magnetic field, convective envelope mass deepening, and internal redistribution of angular momentum are essential to explain the evolution of low-mass stars, and its observational characteristics. Based on population synthesis simulation, we concluded that the solar neighborhood presents a considerable quantity of solar twins when compared with the discovered set nowadays. Altogether we foresee the existence around 400 solar analogs in the solar neighborhood (distance of 100 pc). We also study the angular momentum of solar analogs and twins, in this study we concluded that added angular momentum from a Jupiter type planet, putted in the Jupiter position, is not enough to explain the angular momentum predicted by Kraft law (Kraft 1970)