Дисертації: "Non-stationary tide"

1

凌仕卿 and Shiqing Ling. "Stationary and non-stationary time series models with conditional heteroscedasticity." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1997. http://hub.hku.hk/bib/B31236005.

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2

Ling, Shiqing. "Stationary and non-stationary time series models with conditional heteroscedasticity /." Hong Kong : University of Hong Kong, 1997. http://sunzi.lib.hku.hk/hkuto/record.jsp?B18611953.

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3

Xu, Mengyuan Tracy. "Filtering non-stationary time series by time deformation." Ann Arbor, Mich. : ProQuest, 2008. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3309151.

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Thesis (Ph.D. in Statistical Science)--S.M.U.
Title from PDF title page (viewed Mar. 16, 2009). Source: Dissertation Abstracts International, Volume: 69-04, Section: B, page: 2402. Advisers: Wayne A. Woodward; Henry L. Gray. Includes bibliographical references.

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4

Campbell, N. C. "Statistical methods for non-stationary time series analysis." Thesis, University of Cambridge, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.597266.

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This dissertation is concerned with Bayesian methods for non-stationary time series analysis. Most of the algorithms developed use Markov chain Monte Carlo (MCMC) methods as the means of sampling from the required posteriors. A stochastic version of the Expectation Maximisation (EM) algorithm, the Expectation Sample (ES) algorithm is developed. The performance of this algorithm is compared with EM and other stochastic EM algorithms for parameter estimation of locally stationary time series. The ES algorithm is shown to overcome some of the well documented limitations of the EM algorithm. Non-stationary time series are commonly modelled by segmenting them into a number of independent frames that can be considered stationary. An algorithm is developed whereby these individuals segments can be considered to be dependent on each other. This algorithm is used for the task of noise reduction of a long audio signal and it is shown that the new algorithm gives improved results compared to existing techniques. The time-varying Autoregressive (TVAR) model is introduced as a non-stationary time series model. Basis functions are used to model the TVAR coefficients and an MCMC algorithm developed to perform subset selection on the set of chosen basis functions. Results show that this algorithm is capable of reducing the number of basis functions used to model each TVAR coefficient. The subset selection algorithm is extended to deal with the problem of unknown TVAR model order. Two MCMC algorithms are developed; a reversible jump algorithm and a combined subset selection algorithm. An application to noise reduction of audio signals is considered. The techniques developed previously are extended to account for the fact that the signal is now observed in noise. The algorithm is demonstrated using real audio with added white noise.

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5

Chen, Hao. "Real time model adaptation for non-linear and non-stationary systems." Thesis, University of Reading, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.630445.

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This thesis studies the modelling for the non-linear and non-stationary systems. In a highly non-stationary environment, not only the model coefficients but also the model structure should be adapted with time. A number of novel on-line modeling approaches are proposed in this thesis. The proposed approaches are validated using several benchmark signal processing applications including time series prediction, noise cancellation and channel equalization. First, a novel tunable radial basis function network is proposed. in which the number of nodes (or the model size) of the network is fixed and a new structured node is used to replace the worst performing node whenever the current network does not fit the input data. Two schemes are proposed to optimize t.he structure of the new node: a powerful version based on the quantum particle swarm optimization algorithm and a fast version based on the "gradient search" approach. Secondly, a new online multiple modelling approach is proposed for nonstationary systems. The proposed multimodel approach is based on two level structures of linear sub-models. The advantage of the proposed method is that it is very fast, making it particularly suitable for real time applications. Finally a new adaptive channel equalizer is developed based on minimum biterror- rate. A key issue in the minimum bit-error-rate equalizer is how the probability density function of an associated signed decision variable can be estimated on-line. In the proposed equalizer, a novel online probability density function based on Gaussian mixture model is derived, which has significant better performance than existing approaches.

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6

Muševič, Sašo. "Non-stationary sinusoidal analysis." Doctoral thesis, Universitat Pompeu Fabra, 2013. http://hdl.handle.net/10803/123809.

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Muchos tipos de señales que encontramos a diario pertenecen a la categoría de sinusoides no estacionarias. Una gran parte de esas señales son sonidos que presentan una gran variedad de características: acústicos/electrónicos, sonidos instrumentales harmónicos/impulsivos, habla/canto, y la mezcla de todos ellos que podemos encontrar en la música. Durante décadas la comunidad científica ha estudiado y analizado ese tipo de señales. El motivo principal es la gran utilidad de los avances científicos en una gran variedad de áreas, desde aplicaciones médicas, financiera y ópticas, a procesado de radares o sonar, y también a análisis de sistemas. La estimación precisa de los parámetros de sinusoides no estacionarias es una de las tareas más comunes en procesado digital de señales, y por lo tanto un elemento fundamental e indispensable para una gran variedad de aplicaciones. Las transformaciones de tiempo y frecuencia clásicas son solamente apropiadas para señales con variación lenta de amplitud y frecuencia. Esta suposición no suele cumplirse en la práctica, lo que conlleva una degradación de calidad y la aparición de artefactos. Además, la resolución temporal y frecuencial no se puede incrementar arbitrariamente debido al conocido principio de incertidumbre de Heisenberg. \\ El principal objetivo de esta tesis es revisar y mejorar los métodos existentes para el análisis de sinusoides no estacionarias, y también proponer nuevas estrategias y aproximaciones. Esta disertación contribuye sustancialmente a los análisis sinusoidales existentes: a) realiza una evaluación crítica del estado del arte y describe con gran detalle los métodos de análisis existentes, b) aporta mejoras sustanciales a algunos de los métodos existentes más prometedores, c) propone varias aproximaciones nuevas para el análisis de los modelos sinusoidales existentes i d) propone un modelo sinusoidal muy general y flexible con un algoritmo de análisis directo y rápido.
Many types of everyday signals fall into the non-stationary sinusoids category. A large family of such signals represent audio, including acoustic/electronic, pitched/transient instrument sounds, human speech/singing voice, and a mixture of all: music. Analysis of such signals has been in the focus of the research community for decades. The main reason for such intense focus is the wide applicability of the research achievements to medical, financial and optical applications, as well as radar/sonar signal processing and system analysis. Accurate estimation of sinusoidal parameters is one of the most common digital signal processing tasks and thus represents an indispensable building block of a wide variety of applications. Classic time-frequency transformations are appropriate only for signals with slowly varying amplitude and frequency content - an assumption often violated in practice. In such cases, reduced readability and the presence of artefacts represent a significant problem. Time and frequency resolu

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7

Wong, W. K. "Some contributions to multivariate stationary non-linear time series." Thesis, University of Manchester, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.540596.

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8

Guillaumin, Arthur P. "Quasi-likelihood inference for modulated non-stationary time series." Thesis, University College London (University of London), 2018. http://discovery.ucl.ac.uk/10044853/.

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In this thesis we propose a new class of non-stationary time series models and a quasi-likelihood inference method that is computationally efficient and consistent for that class of processes. A standard class of non-stationary processes is that of locally-stationary processes, where a smooth time-varying spectral representation extends the spectral representation of stationary time series. This allows us to apply stationary estimation methods when analysing slowly-varying non-stationary processes. However, stationary inference methods may lead to large biases for more rapidly-varying non-stationary processes. We present a class of such processes based on the framework of modulated processes. A modulated process is formed by pointwise multiplying a stationary process, called the latent process, by a sequence, called the modulation sequence. Our interest lies in estimating a parametric model for the latent stationary process from observing the modulated process in parallel with the modulation sequence. Very often exact likelihood is not computationally viable when analysing large time series datasets. The Whittle likelihood is a stan- dard quasi-likelihood for stationary time series. Our inference method adapts this function by specifying the expected periodogram of the modulated process for a given parameter vector of the latent time series model, and then fits this quantity to the sample periodogram. We prove that this approach conserves the computational efficiency and convergence rate of the Whittle likelihood under increasing sample size. Finally, our real-data application is concerned with the study of ocean surface currents. We analyse bivariate non-stationary velocities obtained from instruments following the ocean surface currents, and infer key physical quantities from this dataset. Our simulations show the benefit of our modelling and estimation method.

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9

Nguyen, Yen Thi Hong. "Time-frequency distributions : approaches for incomplete non-stationary signals." Thesis, University of Leeds, 2018. http://etheses.whiterose.ac.uk/19681/.

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There are many sources of waveforms or signals existing around us. They can be natural phenomena such as sound, light and invisible like electromagnetic fields, voltage, etc. Getting an insight into these waveforms helps explain the mysteries surrounding our world and the signal spectral analysis (i.e. the Fourier transform) is one of the most significant approaches to analyze a signal. Nevertheless, Fourier analysis cannot provide a time-dependent spectrum description for spectrum-varying signals-non-stationary signal. In these cases, time-frequency distribu- tions are employed instead of the traditional Fourier transform. There have been a variety of methods proposed to obtain the time-frequency representations (TFRs) such as the spectrogram or the Wigner-Ville distribution. The time-frequency distributions (TFDs), indeed, offer us a better signal interpretation in a two-dimensional time-frequency plane, which the Fourier transform fails to give. Nevertheless, in the case of incomplete data, the time-frequency displays are obscured by artifacts, and become highly noisy. Therefore, signal time-frequency features are hardly extracted, and cannot be used for further data processing. In this thesis, we propose two methods to deal with compressed observations. The first one applies compressive sensing with a novel chirp dictionary. This method assumes any windowed signal can be approximated by a sum of chirps, and then performs sparse reconstruction from windowed data in the time domain. A few improvements in computational complexity are also included. In the second method, fixed kernel as well as adaptive optimal kernels are used. This work is also based on the assumption that any windowed signal can be approximately represented by a sum of chirps. Since any chirp's auto-terms only occupy a certain area in the ambiguity domain, the kernel can be designed in a way to remove the other regions where auto-terms do not reside. In this manner, not only cross-terms but also missing samples’ artifact are mitigated significantly. The two proposed approaches bring about a better performance in the time-frequency signature estimations of the signals, which are sim- ulated with both synthetic and real signals. Notice that in this thesis, we only consider the non-stationary signals with frequency changing slowly with time. It is because the signals with rapidly varying frequency are not sparse in time-frequency domain and then the compressive sensing techniques or sparse reconstructions could not be applied. Also, the data with random missing samples are obtained by randomly choosing the samples’ positions and replacing these samples with zeros.

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10

Brat, Guillaume Philippe. "A (max,+) algebra for non-stationary and non-deterministic periodic discrete event systems /." Digital version accessible at:, 1998. http://wwwlib.umi.com/cr/utexas/main.

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11

Neukirch, Maik. "Non Stationary Magnetotelluric Data Processing." Doctoral thesis, Universitat de Barcelona, 2014. http://hdl.handle.net/10803/284932.

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Studies have proven that the desired signal for Magnetotellurics (MT) in the electromagnetic (EM) field can be regarded as 'quasi stationary' (i.e. sufficiently stationary to apply a windowed Fourier transform). However, measured time series often contain environmental noise. Hence, they may not fulfill the stationarity requirement for the application of the Fourier Transform (FT) and therefore may lead to false or unreliable results under methods that rely on the FT. In light of paucity of algorithms of MT data processing in the presence of non stationary noise, it is the goal of this thesis to elaborate a robust, non stationary algorithm, which can compete with sophisticated, state-of-the-art algorithms in terms of accuracy and precision. In addition, I proof mathematically the algorithm's viability and validate its superiority to other codes processing non stationary, synthetic and real MT data. Non stationary EM data may affect the computation of Fourier spectra in unforeseeable manners and consequently, the traditional estimation of the MT transfer functions (TF). The TF estimation scheme developed in this work is based on an emerging nonlinear, non stationary time series analysis tool, called Empirical Mode Decomposition (EMD). EMD decomposes time series into Intrinsic Mode Functions (IMF) in the time-frequency domain, which can be represented by the instantaneous parameters amplitude, phase and frequency. In the first part of my thesis, I show that time slices of well defined IMFs equal time slices of Fourier Series, where the instantaneous parameters of the IMF define amplitude and phase of the Fourier Series parameters. Based on these findings I formulate the theorem that non stationary convolution of an IMF with a general time domain response function translates into a multiplication of the IMF with the respective spectral domain response function, which is explicitly permitted to vary over time. Further, I employ real world MT data to illustrate that a de-trended signal's IMFs can be convolved independently and then be used for further time-frequency analysis as done for MT processing. In the second part of my thesis, I apply the newly formulated theorem to the MT method. The MT method analyses the correlation between the electric and magnetic field due to the conductivity structure of the subsurface. For sufficiently low frequencies (i.e. when the EM field interacts diffusively), the conductive body of the Earth acts as an inductive system response, which convolves with magnetic field variations and results in electric field variations. The frequency representation of this system response is commonly referred to as MT TF and its estimation from measured electric and magnetic time series is summarized as MT processing. The main contribution in this thesis is the design of the MT TF estimation algorithm based on EMD. In contrast to previous works that employ EMD for MT data processing, I (i) point out the advantages of a multivariate decomposition, (ii) highlight the possibility to use instantaneous parameters, and (iii) define the homogenization of frequency discrepancies between data channels. In addition, my algorithm estimates the transfer functions using robust statistical methods such as (i) robust principal component analysis and (ii) iteratively re-weighted least squares regression with a Huber weight function. Finally, TF uncertainties are estimated by iterating the complete robust regression, including the robust weight computation, by means of a bootstrap routine. The proposed methodology is applied to synthetic and real data with and without non stationary character and the results are compared with other processing techniques. I conclude that non stationary noise can heavily affect Fourier based MT data processing but the presented non stationary approach is nonetheless able to extract the impedances correctly even when the other methods fail.

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12

Ng, C. N. "Recursive identification, estimation and forecasting of non-stationary time series." Thesis, Lancaster University, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.383583.

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13

MAGALHAES, MAYSA SACRAMENTO DE. "A SPECTRAL SEQUENTIAL APPROACH TO STUDY NON-STATIONARY TIME SERIE." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1992. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=8787@1.

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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
Diferentes procedimentos têm sido propostos para a modelagem e previsão de séries temporais sendo que nos anos recentes muitos dos métodos mais importantes têm sido formulados na representação espaço de estado. A principal vantagem de tal abordagem é que se pode usar o Filtro de Kalman diretamente para, seqüencialmente, atualizar o vetor de estado. Apresentamos de forma sistemática a abordagem para a previsão de Séries Temporais não- Estacionárias formulada na representação de espaço de estado desenvolvida por P.Young. A novidade desta abordagem não está na natureza dos algoritmos recursivos, e sim na maneira como os hiperparâmetros são obtidos. Modelling and forecasting of Time Series have been approached in many different ways. Lately, the most important approaches have been formulated in a state space framework. The state space representation enables the state vector to be sequentially updated in time via the Kalman filter. In this dissertation, we present in a systematic way an approach to modelling and forecasting of non-stationary time series, formulated in state space terms, and due to P. Young. The novelty of this methodology is neither the nature fo the time series models nor the recursive algorithms, but on how the hyperparameters are estimated
Modelling and forecasting of times Series have been approached in many different ways. Lately, the most important approaches have been formulated in a space framework. The state space representation enables the state vector to be sequencially updated in time via the Kalman filter. In this dissertation, we present in a systematic way an approach to modelling and forecasting of non-stationary time series, formulated in state space terms, and due to P. Young. The novelty of this methodology is neither the nature of the time series models nor the recursive algorithms, but on how the hyperparameteres are estimated

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14

Rajagopalan, Satish. "Detection of Rotor and Load Faults in BLDC Motors Operating Under Stationary and Non-Stationary Conditions." Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/11524.

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Brushless Direct Current (BLDC) motors are one of the motor types rapidly gaining popularity. BLDC motors are being increasingly used in critical high performance industries such as appliances, automotive, aerospace, consumer, medical, industrial automation equipment and instrumentation. Fault detection and condition monitoring of BLDC machines is therefore assuming a new importance. The objective of this research is to advance the field of rotor and load fault diagnosis in BLDC machines operating in a variety of operating conditions ranging from constant speed to continuous transient operation. This objective is addressed as three parts in this research. The first part experimentally characterizes the effects of rotor faults in the stator current and voltage of the BLDC motor. This helps in better understanding the behavior of rotor defects in BLDC motors. The second part develops methods to detect faults in loads coupled to BLDC motors by monitoring the stator current. As most BLDC applications involve non-stationary operating conditions, the diagnosis of rotor faults in non-stationary conditions forms the third and most important part of this research. Several signal processing techniques are reviewed to analyze non-stationary signals. Three new algorithms are proposed that can track and detect rotor faults in non-stationary or transient current signals.

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15

Zhu, Beijia. "Analysis of non-stationary (seasonal/cyclical) long memory processes." Thesis, Paris 1, 2013. http://www.theses.fr/2013PA010013/document.

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La mémoire longue, aussi appelée la dépendance à long terme (LRD), est couramment détectée dans l’analyse de séries chronologiques dans de nombreux domaines, par exemple,en finance, en économétrie, en hydrologie, etc. Donc l’étude des séries temporelles à mémoire longue est d’une grande valeur. L’introduction du processus ARFIMA (fractionally autoregressive integrated moving average) établit une relation entre l’intégration fractionnaire et la mémoire longue, et ce modèle a trouvé son pouvoir de prévision à long terme, d’où il est devenu l’un des modèles à mémoire longue plus populaires dans la littérature statistique. Précisément, un processus à longue mémoire ARFIMA (p, d, q) est défini comme suit : Φ(B)(I − B)d (Xt − µ) = Θ(B)εt, t ∈ Z, où Φ(z) = 1 − ϕ1z − · · · − ϕpzp et Θ(z) = 1 + · · · + θ1zθpzq sont des polynômes d’ordre p et q, respectivement, avec des racines en dehors du cercle unité; εt est un bruit blanc Gaussien avec une variance constante σ2ε. Lorsque d ∈ (−1/2,1/2), {Xt} est stationnaire et inversible. Cependant, l’hypothèse a priori de la stationnarité des données réelles n’est pas raisonnable. Par conséquent, de nombreux auteurs ont fait leurs efforts pour proposer des estimateurs applicables au cas non-stationnaire. Ensuite, quelques questions se lèvent : quel estimateurs doit être choisi pour applications, et à quoi on doit faire attention lors de l’utilisation de ces estimateurs. Donc à l’aide de la simulation de Monte Carlo à échantillon fini, nous effectuons une comparaison complète des estimateurs semi-paramétriques, y compris les estimateurs de Fourier et les estimateurs d’ondelettes, dans le cadre des séries non-stationnaires. À la suite de cette étude comparative, nous avons que (i) sans bonnes échelles taillées, les estimateurs d’ondelettes sont fortement biaisés et ils ont généralement une performance inférieure à ceux de Fourier; (ii) tous les estimateurs étudiés sont robustes à la présence d’une tendance linéaire en temps dans le niveau de {Xt} et des effets GARCH dans la variance de {Xt}; (iii) dans une situation où le probabilité de transition est bas, la consistance des estimateurs quand même tient aux changements de régime dans le niveau de {Xt}, mais les changements ont une contamination au résultat d’estimation; encore, l’estimateur d’ondelettes de log-regression fonctionne mal dans ce cas; et (iv) en général, l’estimateur complètement étendu de Whittle avec un polynôme locale (fully-extended local polynomial Whittle Fourier estimator) est préféré pour une utilisation pratique, et cet estimateur nécessite une bande (i.e. un nombre de fréquences utilisés dans l’estimation) plus grande que les autres estimateurs de Fourier considérés dans ces travaux
Long memory, also called long range dependence (LRD), is commonly detected in the analysis of real-life time series data in many areas; for example, in finance, in econometrics, in hydrology, etc. Therefore the study of long-memory time series is of great value. The introduction of ARFIMA (fractionally autoregressive integrated moving average) process established a relationship between the fractional integration and long memory, and this model has found its power in long-term forecasting, hence it has become one of the most popular long-memory models in the statistical literature. Specifically, an ARFIMA(p,d,q) process X, is defined as follows: cD(B)(I - B)d X, = 8(B)c, , where cD(z)=l-~lz-•••-~pzP and 8(z)=1-B1z- .. •-Bqzq are polynomials of order $p$ and $q$, respectively, with roots outside the unit circle; and c, is Gaussian white noise with a constant variance a2 . When c" X, is stationary and invertible. However, the a priori assumption on stationarity of real-life data is not reasonable. Therefore many statisticians have made their efforts to propose estimators applicable to the non-stationary case. Then questions arise that which estimator should be chosen for applications; and what we should pay attention to when using these estimators. Therefore we make a comprehensive finite sample comparison of semi-parametric Fourier and wavelet estimators under the non-stationary ARFIMA setting. ln light of this comparison study, we have that (i) without proper scale trimming the wavelet estimators are heavily biased and the y generally have an inferior performance to the Fourier ones; (ii) ail the estimators under investigation are robust to the presence of a linear time trend in levels of XI and the GARCH effects in variance of XI; (iii) the consistency of the estimators still holds in the presence of regime switches in levels of XI , however, it tangibly contaminates the estimation results. Moreover, the log-regression wavelet estimator works badly in this situation with small and medium sample sizes; and (iv) fully-extended local polynomial Whittle Fourier (fextLPWF) estimator is preferred for a practical utilization, and the fextLPWF estimator requires a wider bandwidth than the other Fourier estimators

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16

Guan, Yunpeng. "Velocity Synchronous Approaches for Planetary Gearbox Fault Diagnosis under Non-Stationary Conditions." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/38636.

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Time-frequency methods are widely used tools to diagnose planetary gearbox fault under non-stationary conditions. However, the existing time-frequency methods still have some problems, such as smearing effect and cross-term interference, and these problems limit the effectiveness of the existing time-frequency methods in planetary gearbox fault diagnosis under non-stationary conditions. To address the aforementioned problems, four time-frequency methods are proposed in this thesis. As nowadays a large portion of the industrial equipment is equipped with tachometers, the first three methods are for the cases that the shaft rotational speed is easily accessible and the last method is for the cases of shaft rotational speed is not easily accessible. The proposed methods are itemized as follows: (1) The velocity synchronous short-time Fourier transform (VSSTFT), which is a type of linear transform based on the domain mappings and short-time Fourier transform to address the smear effect of the existing linear transforms under known time-varying speed conditions; (2) The velocity synchrosqueezing transform (VST), which is a type of remapping method based on the domain mapping and synchrosqueezing transform to address the smear effect of existing remapping methods under known time-varying speed conditions; (3) The velocity synchronous bilinear distribution (VSBD), which is a type of bilinear distribution based on the generalized demodulation and Cohen’s class bilinear distribution to address the smear effect and cross-term interference of existing bilinear distributions under known time-varying speed conditions and (4) The velocity synchronous linear chirplet transform (VSLCT), which is a non-parametric combined approach of linear transform and concentration-index-guided parameter determination to provide a smear-free and cross-term-free TFR under unknown time-varying speed conditions. In this work, simple algorithms are developed to avoid the signal resampling process required by the domain mappings or demodulations of the first three methods (i.e., the VSSTFT, VST and VSBD). They are designed to have different resolutions, readabilities, noise tolerances and computational efficiencies. Therefore, they are capable to adapt different application conditions. The VSLCT, as a kind of linear transform, is designed for unknown rotational speed conditions. It utilizes a set of shaft-rotational-speed-synchronous bases to address the smear problem and it is capable to dynamically determine the signal processing parameters (i.e., window length and normalized angle) to provide a clear TFR with desirable time-frequency resolution in response to condition variations. All of the proposed methods in this work are smear-free and cross-term-free, the TFRs generated by the methods are clearer and more precise compared with the existing time-frequency methods. The faults of planetary gearboxes, if any, can be diagnosed by identifying the fault-induced components from the obtained TFRs. The four methods are all newly applied to fault diagnosis. The effectiveness of them has been validated using both simulated and experimental vibration signals of planetary gearboxes collected under non-stationary conditions.

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17

Tadjuidje, Kamgaing Joseph. "Competing neural networks as models for non stationary financial time series changepoint analysis /." [S.l. : s.n.], 2005. http://deposit.ddb.de/cgi-bin/dokserv?idn=974108014.

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18

Lee, Jong-Sik. "Time-varying filter modelling and time-frequency characterisation of non-stationary sound fields due to a moving source." Thesis, University of Southampton, 1989. https://eprints.soton.ac.uk/52248/.

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This thesis deals with the problems of modelling, interpretation and estimation of `non-stationary' processes with particular reference to acoustic problems. A common assumption in the modelling and analysis of a random process is that the process is `stationary'. Such an assumption may be a satisfactory approximation in many instances, but there are situations in which the processes are obviously non-stationary. In particular many physical non-stationary processes exhibit a `frequency-modulated' structure. An important example of such processes is the sound perceived by an observer due to a moving source emitting a random signal. In the thesis two methods are studied for the characterisation of such non-stationary processes; i) `time-frequency' spectral characterisation and ii) time-varying filter modelling. Two major candidates for `time-frequency' (time-varying) spectral characterisation of non-stationary processes are the Wigner-Ville spectrum and Priestley's evolutionary spectrum. Properties, prediction and estimation of the two time-frequency spectra and the relation between them are discussed. The time-frequency spectra of the sound field due to a moving source are predicted and these spectra are used as the basis for estimation of the acoustic directionality pattern of the source. As to the time-varying filter modelling of such non-stationary processes, a technique called the `covariance-equivalent' method is discussed. The covariance-equivalent technique is used to model the sound field due to a moving source emitting a random signal in single-path/single-sensor cases. The covariance-equivalent method, which has only been applicable to single-component processes, is extended to include the sound field in multi-path/multi-sensor cases by using the concept of the complex envelope (complex process). Finally estimation problems of practical importance, including that of (i) the source acoustic directionality pattern and (ii) time-varying delay estimation problems, are formulated and solved in terms of the covariance-equivalent models, and simulation studies are also performed. The simulation results justify that the covariance-equivalent method is an effective characterisation of such non-stationary processes.

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19

Ristic, Branko. "Some aspects of signal dependent and higher-order time-frequency and time-scale analysis of non-stationary signals." Thesis, Queensland University of Technology, 1995.

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20

Moskowitz, David. "Automatically Defined Templates for Improved Prediction of Non-stationary, Nonlinear Time Series in Genetic Programming." NSUWorks, 2016. http://nsuworks.nova.edu/gscis_etd/953.

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Soft methods of artificial intelligence are often used in the prediction of non-deterministic time series that cannot be modeled using standard econometric methods. These series, such as occur in finance, often undergo changes to their underlying data generation process resulting in inaccurate approximations or requiring additional human judgment and input in the process, hindering the potential for automated solutions. Genetic programming (GP) is a class of nature-inspired algorithms that aims to evolve a population of computer programs to solve a target problem. GP has been applied to time series prediction in finance and other domains. However, most GP-based approaches to these prediction problems do not consider regime change. This paper introduces two new genetic programming modularity techniques, collectively referred to as automatically defined templates, which better enable prediction of time series involving regime change. These methods, based on earlier established GP modularity techniques, take inspiration from software design patterns and are more closely modeled after the way humans actually develop software. Specifically, a regime detection branch is incorporated into the GP paradigm. Regime specific behavior evolves in a separate program branch, implementing the template method pattern. A system was developed to test, validate, and compare the proposed approach with earlier approaches to GP modularity. Prediction experiments were performed on synthetic time series and on the S&P 500 index. The performance of the proposed approach was evaluated by comparing prediction accuracy with existing methods. One of the two techniques proposed is shown to significantly improve performance of time series prediction in series undergoing regime change. The second proposed technique did not show any improvement and performed generally worse than existing methods or the canonical approaches. The difference in relative performance was shown to be due to a decoupling of reusable modules from the evolving main program population. This observation also explains earlier results regarding the inferior performance of genetic programming techniques using a similar, decoupled approach. Applied to financial time series prediction, the proposed approach beat a buy and hold return on the S&P 500 index as well as the return achieved by other regime aware genetic programming methodologies. No approach tested beat the benchmark return when factoring in transaction costs.

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21

Fossi, Fotsi Yannick. "Dynamique morpho-sédimentaire de l’estuaire du Wouri, Cameroun." Thesis, La Rochelle, 2022. http://www.theses.fr/2022LAROS012.

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L’estuaire du Wouri, situé au coeur du Golfe de Guinée et ouvert sur l’océan Atlantique est soumis à un large éventail d’influence atmosphérique, océanique, continentale et anthropique à différentes échelles de temps (court et long-terme) contrôlant son évolution. La première partie de cette thèse, axée sur des archives remontant au 20ème siècle, a permis de reconstituer l’histoire de l’évolution du littoral estuarien du Wouri. Parallèlement, pour déterminer les tendances d’évolution des hauteurs d’eau, quantifier et qualifier la cinématique du trait de côte et des fonds dans l’estuaire, un travail d’inventaire, de numérisation et d’analyse des documents historiques a été réalisé. Ceci a permis d’enregistrer une évolution du niveau moyen à un rythme d’environ 25mm/an en 17 ans (2002 – 2019). Les résultats ont révélé une prédominance des variations dominées par l’érosion en aval et inversement par l’accrétion en amont, sur la période de 64 ans (1948-2012). Ces tendances sont accentuées par la présence de facteurs amplificateurs (pression anthropique et changement climatique). Afin d’étudier les processus hydrodynamiques et sédimentaires à court terme, une modélisation numérique de la propagation de la marée et la distribution des salinités et des sédiments fins a été réalisée à l’aide de TELEMAC 3D (calibré et validé grâce aux mesures in-situ acquises au cours de l’année 2019). La marée a montré une asymétrie dominée par le jusant dans sa partie inférieure et inversement par le flot dans sa partie supérieure. La distribution de la salinité a permis de caractériser l’estuaire de bien mélangé en vive-eau, particulièrement en étiage à stratifié en morte eau, particulièrement en période de crue. Les variations saisonnières, du régime fluvial ont montré une migration longitudinale de la position de la zone de turbidité maximale : déplacement en amont pendant les étiages et en aval pendant les crues avec pour conséquence une exportation massive de sédiments dans la partie intermédiaire et aval de l’estuaire. Dans un contexte actuel du changement climatique associé aux forts impacts anthropiques, cette étude souligne la nécessité de l’utilisation des archives historiques, de données in-situ couplées à un modèle numérique pour mieux comprendre l’évolution passée et actuelle de l’hydrodynamique et de la dynamique sédimentaire
The Wouri estuary, located in the heart of the Gulf of Guinea and open to the Atlantic Ocean, is subject to a wide range of atmospheric, oceanic, continental and anthropic influences at different time scales (short and long term) controlling its evolution. The first part of this thesis, based on archives dating back to the 20th century, allows us to reconstruct the history of the evolution of the Wouri estuary coastline. At the same time, in order to determine the evolution trends of the water levels, to quantify and qualify the kinematics of the coastline and the bottoms in the estuary, an inventory, digitization and analysis of historical documents was carried out. This allowed to record an evolution of the average level at a rate of about 25mm/year in 17 years (2002 - 2019). The results revealed a predominance of variations dominated by erosion downstream and conversely by accretion upstream, over the 64-year period (1948-2012). These trends are accentuated by the presence of amplifying factors (anthropogenic pressure and climate change). In order to study the hydrodynamic and sedimentary processes in the short term, a numerical modeling of the tidal propagation and the distribution of salinities and fine sediments was performed using TELEMAC 3D (calibrated and validated thanks to in-situ measurements acquired during 2019). The tide showed an asymmetry dominated by the ebb in its lower part and inversely by the flood in its upper part. The distribution of salinity allowed to characterize the estuary from well mixed in spring tide, particularly in low water to stratified in neap tide, particularly in flood period. Seasonal variations of the river regime have shown a longitudinal migration of the position of the maximum turbidity zone : upstream during low water and downstream during high water with a massive export of sediments in the intermediate and downstream part of the estuary. In a current context of climate change associated with strong anthropogenic impacts, this study highlights the need to use historical archives, in-situ data coupled with a numerical model to better understand the past and present evolution of hydrodynamics and sediment dynamics

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22

Gajecka-Mirek, Elżbieta. "Estimation of the parameters for non-stationary time series with long memory and heavy tails using weak dependence condition." Doctoral thesis, Katowice : Uniwersytet Śląski, 2015. http://hdl.handle.net/20.500.12128/5928.

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Wnioskowanie statystyczne dla nieznanych rozkładów statystyk lub estymatorów można oprzeć na rozkładach asymptotycznych. Niestety, w przypadku danych zależnych, takie procedury statystyczne są¸ niejednokrotnie nieefektywne. Różne są¸ tego przyczyny, np. zbyt ma la liczba danych, nieznana postać rozkładu asymptotycznego, zbyt wolna zbieżność do rozkładu asymptotycznego. Od początku lat osiemdziesiątych ubiegłego wieku intensywnie prowadzone są badania nad rozwojem tzw. metod resamplingowych. Za pomocą tychże metod można bezpośrednio przybliżać nieznane rozkłady statystyk i estymatorów. Idea resamplingu jest prosta. Obliczamy replikacje estymatora i z tych replikacji wyznaczamy rozkład empiryczny tzw. rozkład resamplingowy. Problem, z którym trzeba się zmierzyć badając procedury resamplingowe to ich zgodność, tzn. czy rozkład resamplingowy jest bliski prawdziwemu rozkładowi ? Metod resamplingowych jest wiele. Ich zgodność w przypadku obserwacji niezależnych została dogłębnie zbadana. Przypadek danych stacjonarnych ze swoistą strukturą zależności tzn. silnie mieszających także został zbadany. Przedmiotem intensywnych prac badaczy był również resampling dla niestacjonarnych szeregów czasowych ze specyﬁczną formą niestacjonarności tzn. okresowych i prawie okresowych. Ostatnie badania nad metodami resamplingowymi koncentrują się głównie na szeregach czasowych ze zdeﬁniowana¸ przez Paula Doukhana słabą zależnością. W niniejszej pracy został przedstawiony model dla szeregów czasowych, które maja¸ bardzo specyﬁczne własności tzn.: posiadają długa¸ pamięć, ciężkie ogony (stabilne lub GED) oraz strukturę okresową. Taki model może mieć naturalne zastosowanie w wielu dziedzinach np.: energetyce, wibromechanice, telekomunikacji, klimatologii jak również w ekonomii. Celem pracy jest pokazanie twierdzeń dotyczących zgodności estymatora jednej z metod resamplingowych dla funkcji średniej we wspomnianych powyżej szeregach czasowych. Okazuje się, że jedyną metodą resamplingową, którą można zastosować do danych z długą pamięcią jest subsampling. Polega ona na wyborze z obserwacji wszystkich możliwych podciągów o pewnej długości i wyznaczaniu estymatora na tych podciągach. W pracy sformułowano i udowodniono centralne twierdzenia graniczne, niezbędne do udowodnienia zgodności subsamplingu. Ponadto przedstawiony został przegląd dotychczasowych rezultatów dotyczących metod resamplingowych w szeregach czasowych.

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23

Roberts, Geoff. "Classification of non-stationary signals using time-frequency representations and multiple hypotheses tests : an application to humpback whale songs." Thesis, Queensland University of Technology, 1999.

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24

Tino, Peter, Christian Schittenkopf, and Georg Dorffner. "Temporal pattern recognition in noisy non-stationary time series based on quantization into symbolic streams. Lessons learned from financial volatility trading." SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business, 2000. http://epub.wu.ac.at/1680/1/document.pdf.

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In this paper we investigate the potential of the analysis of noisy non-stationary time series by quantizing it into streams of discrete symbols and applying finite-memory symbolic predictors. The main argument is that careful quantization can reduce the noise in the time series to make model estimation more amenable given limited numbers of samples that can be drawn due to the non-stationarity in the time series. As a main application area we study the use of such an analysis in a realistic setting involving financial forecasting and trading. In particular, using historical data, we simulate the trading of straddles on the financial indexes DAX and FTSE 100 on a daily basis, based on predictions of the daily volatility differences in the underlying indexes. We propose a parametric, data-driven quantization scheme which transforms temporal patterns in the series of daily volatility changes into grammatical and statistical patterns in the corresponding symbolic streams. As symbolic predictors operating on the quantized streams we use the classical fixed-order Markov models, variable memory length Markov models and a novel variation of fractal-based predictors introduced in its original form in (Tino, 2000b). The fractal-based predictors are designed to efficiently use deep memory. We compare the symbolic models with continuous techniques such as time-delay neural networks with continuous and categorical outputs, and GARCH models. Our experiments strongly suggest that the robust information reduction achieved by quantizing the real-valued time series is highly beneficial. To deal with non-stationarity in financial daily time series, we propose two techniques that combine ``sophisticated" models fitted on the training data with a fixed set of simple-minded symbolic predictors not using older (and potentially misleading) data in the training set. Experimental results show that by quantizing the volatility differences and then using symbolic predictive models, market makers can generate a statistically significant excess profit. However, with respect to our prediction and trading techniques, the option market on the DAX does seem to be efficient for traders and non-members of the stock exchange. There is a potential for traders to make an excess profit on the FTSE 100. We also mention some interesting observations regarding the memory structure in the studied series of daily volatility differences. (author's abstract)
Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

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25

Cálipo, Leonardo Gurgel. "Análise do problema de controle de estoques dinâmico para demanda não estacionária e lead-time positivo." Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/3/3136/tde-22052015-155307/.

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O problema de controle de estoques com demanda não estacionária e lead-time positivo tem se tornado cada vez mais relevante em virtude da crescente tendência de redução do ciclo de vida dos produtos e internacionalização das cadeias de suprimentos. Embora haja uma solução exata para a minimização do custo esperado da política de estoques para este cenário, baseado no método de programação dinâmica, o custo computacional deste método ainda é considerado elevado. Este trabalho detalha e avalia através de simulação o método exato e duas aproximações para a minimização do custo esperado da política de estoques, em termos do desempenho em custo e eficiência computacional. Os resultados experimentais permitem a análise dos métodos disponíveis. Enquanto a abordagem heurística de Bollapragada e Morton, que utiliza o nivelamento da demanda não estacionária, perde desempenho de custo com o aumento do lead-time, a nova heurística proposta, que aproxima os parâmetros da política ótima por valores limitantes, produz resultados sucessivamente melhores com o aumento do lead-time.
The inventory control problem with nonstationary demand and positive lead-time has become increasingly important due to the growing trend of reduction in product life cycle and internationalization of the supply chain. Although there is an exact solution to the minimization of the expected cost of inventory policy on this environment, through the method of dynamic programming, the computational cost of this method is still considered high. This work details and evaluates through simulation the exact method and two heuristic solutions for the minimization of expected cost of inventory policy, in terms of cost performance and computational efficiency. The experimental results allow the analysis of the available methods. While the Bollapragada and Morton heuristic approach, which levels the non-stationary demand, decreases the cost performance when lead-time is increased, the new heuristic proposed, that approximates the optimal policy parameters by limiting values, successively produces better results with increasing lead-times.

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Маринич, Тетяна Олександрівна, Татьяна Александровна Маринич, Tetiana Oleksandrivna Marynych, Людмила Дмитрівна Назаренко, Людмила Дмитриевна Назаренко та Liudmyla Dmytrivna Nazarenko. "Порівняльний аналіз методів моделювання нестаціонарних часових рядів". Thesis, Харьківський національний університет ім. В.Н. Каразіна, 2016. http://essuir.sumdu.edu.ua/handle/123456789/68628.

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27

Wei, Jianxin. "On Bootstrap Evaluation of Tests for Unit Root and Cointegration." Doctoral thesis, Uppsala universitet, Statistiska institutionen, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-233885.

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This thesis is comprised of five papers that all relate to bootstrap methodology in analysis of non-stationary time series. The first paper starts with the fact that the Dickey-Fuller unit root test using asymptotic critical value has bad small sample performance. The small sample correction proposed by Johansen (2004) and bootstrap are two effective methods to improve the performance of the test. In this paper we compare these two methods as well as analyse the effect of bias-adjusting through a simulation study. We consider AR(1) and AR(2) models and both size and power properties are investigated. The second paper studies the asymptotic refinement of the bootstrap cointegration rank test. We expand the test statistic of a simplified VECM model and a Monte Carlo simulation was carried out to verify that the bootstrap test gives asymptotic refinement. The third paper focuses on the number of bootstrap replicates in bootstrap Dickey-Fuller unit root test. Through a simulation study, we find that a small number of bootstrap replicates are sufficient for a precise size, but, with too small number of replicates, we will lose power when the null hypothesis is not true. The fourth and last paper of the thesis concerns unit root test in panel setting focusing on the test proposed by Palm, Smeekes and Urbain (2011). In the fourth paper, we study the robustness of the PSU test with comparison with two representative tests from the second generation panel unit root tests. In the last paper, we generalise the PSU test to the model with deterministic terms. Two different methods are proposed to deal with the deterministic terms, and the asymptotic validity of the bootstrap procedure is theoretically checked. The small sample properties are studied by simulations and the paper is concluded by an empirical example.

Ogiltigt ISBN: 978-91-554-9069-0

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28

Mayer, Ulrike [Verfasser], and Henryk [Akademischer Betreuer] Zähle. "Functional weak limit theorem for a local empirical process of non-stationary time series and its application to von Mises-statistics / Ulrike Mayer ; Betreuer: Henryk Zähle." Saarbrücken : Saarländische Universitäts- und Landesbibliothek, 2019. http://d-nb.info/119175555X/34.

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29

Müller, Philipp [Verfasser], Holger [Akademischer Betreuer] Kantz, Marc [Gutachter] Timme, and Jürgen [Gutachter] Kurths. "Extreme value analysis of non-stationary time series : Quantifying climate change using observational data throughout Germany / Philipp Müller ; Gutachter: Marc Timme, Jürgen Kurths ; Betreuer: Holger Kantz." Dresden : Technische Universität Dresden, 2019. http://d-nb.info/1226900984/34.

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30

Mayer, Ulrike Verfasser], and Henryk [Akademischer Betreuer] [Zähle. "Functional weak limit theorem for a local empirical process of non-stationary time series and its application to von Mises-statistics / Ulrike Mayer ; Betreuer: Henryk Zähle." Saarbrücken : Saarländische Universitäts- und Landesbibliothek, 2019. http://nbn-resolving.de/urn:nbn:de:bsz:291--ds-281226.

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31

Guégan, Dominique. "Modèles bilinéaires et polynomiaux de séries chronologiques : étude probabiliste et analyse statistique." Grenoble 1, 1988. http://tel.archives-ouvertes.fr/tel-00330671.

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Cette thèse présente l'étude probabiliste et statistique approfondie des modèles bilinéaires à temps discret. On étudie ces modèles à partir de différentes approches (discrète, markovienne). On trouve tout d'abord une présentation globale des modèles non linéaires, la description des outils probabilistes utiles à l'étude des modèles non linéaires, ainsi qu'une présentation des modèles bilinéaires à partir de simulations permettant de mettre en évidence leurs principales caractéristiques trajectorielles. L'approche markovienne s'avère beaucoup plus puissante que l'approche directe. Nous démontrons l'existence d'une représentation markovienne sous la forme d'un modèle polynomial affine en l'état; nous donnons des critères pour la minimalité et l'inversibilité de ces représentations. Sur le plan statistique, nous avons montre la convergence presque sure des estimateurs des moindres carrés. D'autres estimateurs sont aussi envisagés permettant de mettre en place des tests d'adéquation de modèles. Certains travaux de l'auteur (huit articles) ont été publiés et sont regroupés dans l'annexe.

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32

Turner, Kenneth James. "Higher-order filtering for nonlinear systems using symmetric tensors." Thesis, Queensland University of Technology, 1999.

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33

Firla, Marcin. "Automatic signal processing for wind turbine condition monitoring. Time-frequency cropping, kinematic association, and all-sideband demodulation." Thesis, Université Grenoble Alpes (ComUE), 2016. http://www.theses.fr/2016GREAT006/document.

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Cette thèse propose trois méthodes de traitement du signal orientées vers la surveillance d’état et le diagnostic. Les techniques proposées sont surtout adaptées pour la surveillance d’état, effectuée à la base de vibrations, des machines tournantes qui fonctionnent dans des conditions d’opération non-stationnaires comme par exemple les éoliennes mais elles ne sont pas limitées à un tel usage. Toutes les méthodes proposées sont des algorithmes automatiques et gérés par les données.La première technique proposée permet de sélectionner la partie la plus stationnaire d’un signal en cadrant la représentation temps-fréquence d’un signal.La deuxième méthode est un algorithme pour l’association des dispositions spectrales, des séries harmoniques et des séries à bandes latérales avec des fréquences caractéristiques provennant du cinématique d'un système analysé. Cette méthode propose une approche unique dédiée à l’élément roulant du roulement qui permet de surmonter les difficultés causées par le phénomène de glissement.La troisième technique est un algorithme de démodulation de bande latérale entière. Elle fonctionne à la base d’un filtre multiple et propose des indicateurs de santé pour faciliter une évaluation d'état du système sous l’analyse.Dans cette thèse, les méthodes proposées sont validées sur les signaux simulés et réels. Les résultats présentés montrent une bonne performance de toutes les méthodes
This thesis proposes a three signal-processing methods oriented towards the condition monitoring and diagnosis. In particular the proposed techniques are suited for vibration-based condition monitoring of rotating machinery which works under highly non-stationary operational condition as wind turbines, but it is not limited to such a usage. All the proposed methods are automatic and data-driven algorithms.The first proposed technique enables a selection of the most stationary part of signal by cropping time-frequency representation of the signal.The second method is an algorithm for association of spectral patterns, harmonics and sidebands series, with characteristic frequencies arising from kinematic of a system under inspection. This method features in a unique approach dedicated for rolling-element bearing which enables to overcome difficulties caused by a slippage phenomenon.The third technique is an all-sideband demodulation algorithm. It features in a multi-rate filter and proposes health indicators to facilitate an evaluation of the condition of the investigated system.In this thesis the proposed methods are validated on both, simulated and real-world signals. The presented results show good performance of all the methods

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34

Boiardi, Andrea. "Study of a Procedure for Unit Load Transport Simulation." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022.

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Accurate transport simulation is necessary to determine the probability that a unitary load might be disassembled or subject to severe structural changes while travelling to its destination. With the evaluations that come from accurate transport simulation companies might be able, for example, to optimize for the amount of plastic to use in the wrapping process of a unitary load. Other than the obvious immense cost reduction given by a lower incidence of product-ruining accidents during transport, optimizing plastic usage would also help avoid over-wrapping, resulting in better environmental outcomes. Currently, multi-axial simulation is more and more being adopted by companies in the hopes of representing more closely the stresses a load is subject to during transport. However, there is no international standard outlining a procedure that one might follow in order to do so, yet. This work starts from the identification of the best methods to record simulation-oriented transport data precisely and reliably. Some recordings of different types of transport have been conducted to highlight their different nature and to show an example of how to use a sensor in different contexts. Then, the main existing methods for simulating transport are analyzed and used to create a new procedure for the selection of the most suitable one, mainly depending on the availability of technological resources and transport data. Lastly, considerations about possible future improvements on this work are presented.

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35

Hili, Ouagnina. "Contribution à l'estimation des modèles de séries temporelles non linéaires." Université Louis Pasteur (Strasbourg) (1971-2008), 1995. http://www.theses.fr/1995STR13169.

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Le but de la these est d'effectuer l'inference statistique d'une classe generale de modeles de series temporelles non lineaires. Notre contribution consiste d'abord a determiner des conditions assurant l'existence d'une loi stationnaire, l'existence des moments de cette loi stationnaire et la forte melangeance de tels modeles. Nous etablissons ensuite les proprietes asymptotiques de l'estimateur du minimum de distance d'hellinger du parametre d'interet. La robustesse de cet estimateur est egalement envisagee. Nous examinons aussi, via la methode des moindres carres, les proprietes asymptotiques des estimateurs des coefficients des modeles autoregressifs a seuils

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36

Sànchez, Pérez Andrés. "Agrégation de prédicteurs pour des séries temporelles, optimalité dans un contexte localement stationnaire." Thesis, Paris, ENST, 2015. http://www.theses.fr/2015ENST0051/document.

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Cette thèse regroupe nos résultats sur la prédiction de séries temporelles dépendantes. Le document comporte trois chapitres principaux où nous abordons des problèmes différents. Le premier concerne l’agrégation de prédicteurs de décalages de Bernoulli Causales, en adoptant une approche Bayésienne. Le deuxième traite de l’agrégation de prédicteurs de ce que nous définissions comme processus sous-linéaires. Une attention particulaire est portée aux processus autorégressifs localement stationnaires variables dans le temps, nous examinons un schéma de prédiction adaptative pour eux. Dans le dernier chapitre nous étudions le modèle de régression linéaire pour une classe générale de processus localement stationnaires
This thesis regroups our results on dependent time series prediction. The work is divided into three main chapters where we tackle different problems. The first one is the aggregation of predictors of Causal Bernoulli Shifts using a Bayesian approach. The second one is the aggregation of predictors of what we define as sub-linear processes. Locally stationary time varying autoregressive processes receive a particular attention; we investigate an adaptive prediction scheme for them. In the last main chapter we study the linear regression problem for a general class of locally stationary processes

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37

Souza, Leandro Teixeira Lopes de. "Modelos de séries temporais com coeficientes variando no tempo." Universidade Federal de São Carlos, 2009. https://repositorio.ufscar.br/handle/ufscar/4528.

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Made available in DSpace on 2016-06-02T20:06:02Z (GMT). No. of bitstreams: 1 2524.pdf: 3173626 bytes, checksum: 444d75f97bd088459e470db31df717a5 (MD5) Previous issue date: 2009-02-26
Financiadora de Estudos e Projetos
In this work they are presented extensions of Auto Regressive and Auto Regressive Conditional Heteroscedasticity models with coefficients varying in time. These coefficients have been used as models for non stationary real time series, specially for financial series. The objective of this work is to present the models and the techniques involved in estimating time-varying coefficients, moreover, it is made an introduction to financial modeling and some suggestions in order to facilitate implementation and use of models with time-varying coefficients. The simulation studies and the application on real data showed that the models have great potential to be exploited in the analysis of non-stationary series. The suggestions in confidence band and forecasting for the Auto regressive models with time-varying coefficients enable the use of models in financial data and other series that show a non-stationary characteristic. The modified algorithm for estimation of ARCH models varying in time was to increase the rate of convergence. The creation of a method for forecasting for ARCH models require a deeper study, although the algorithm has shown promising results in simulation study, giving some evidences that it can be applied in real situation. Finally, the contributions in the creation of functions for a free software that facilitate the use and the analysis of the models studied and the use of the proposed methods.
No presente trabalho são apresentadas extensões dos modelos Auto Regressivo e Auto Regressivo Condicionalmente Heteroscedasticos com coeficientes variando ao longo do tempo. Estes têm sido utilizados como modelos para séries temporais reais não estacionárias, em especial as séries financeiras. O objetivo desse trabalho é apresentar os modelos e as técnicas envolvidas para estimar esses coeficientes que variam no tempo, além disso, é feito uma introdução a modelagem financeira e algumas sugestões para facilitar a aplicação e utilização dos modelos com coeficientes variando no tempo. Os estudos de simulação e a aplicação em dados reais mostraram que os modelos têm um grande potencial a ser explorados na análise de séries não estacionárias. As sugestões de banda de confiança e previsão para os modelos Auto Regressivos com coeficientes variando no tempo viabilizam a utilização dos modelos em dados financeiros e outras séries que apresentam uma característica de não estacionariedade. As modificações no algoritmo de estimação dos modelos ARCH variando no tempo foram para aumentar a taxa de convergência. A criação de um método para previsão dos modelos ARCH necessitam de um estudo mais profundo, porém o algoritmo mostrou resultados promissores no estudo de simulação, dando alguns indícios de que pode ser aplicada na prática. Por fim, as contribuições na criação de funções para um software livre que facilitam a utilização e a análise dos modelos estudados bem como a utilização dos métodos propostos.

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38

Lambert-Lacroix, Sophie. "Fonction d'autocorrélation partielle des processus à temps discret non stationnaires et applications." Phd thesis, Université Joseph Fourier (Grenoble), 1998. http://tel.archives-ouvertes.fr/tel-00004893.

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Cette thèse présente la fonction d'autocorrélation partielle d'un processus non stationnaire ainsi que des applications dans le domaine spectral et dans le cadre des processus périodiquement corrélés. Après avoir introduit cette fonction, nous montrons qu'elle caractérise la structure au second ordre des processus non stationnaires. Son intérêt est d'être facilement identifiable par rapport à la fonction d'autocovariance qui doit être de type positif. De plus elle conduit de façon naturelle à la définition d'un nouveau spectre dépendant du temps. Ce dernier décrit, à chaque instant, une situation stationnaire dans laquelle le présent est corrélé avec le passé de la même façon que le processus non stationnaire au même instant. L'étude des propriétés de ce spectre permet de le comparer à deux autres de même nature. On se restreint ensuite à la classe particulière des processus périodiquement corrélés. La fonction d'autocorrélation partielle fournit une nouvelle paramétrisation qui permet, en particulier, d'étendre de façon naturelle la méthode du maximum d'entropie à cette situation. Enfin nous considérons l'estimation autorégressive dans le cadre de ces processus en proposant une estimation adéquate de ces paramètres. La comparaison avec les procédures existantes est effectuée en regroupant certaines d'entre elles dans une même méthodologie mais aussi par simulation. Nous étudions également le lien entre ces approches et celles du cas vectoriel stationnaire.

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39

Kamanu, Timothy Kevin Kuria. "Location-based estimation of the autoregressive coefficient in ARX(1) models." Thesis, University of the Western Cape, 2006. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_9551_1186751947.

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In recent years, two estimators have been proposed to correct the bias exhibited by the leastsquares (LS) estimator of the lagged dependent variable (LDV) coefficient in dynamic regression models when the sample is finite. They have been termed as &lsquo
mean-unbiased&rsquo
and &lsquo
medianunbiased&rsquo
estimators. Relative to other similar procedures in the literature, the two locationbased estimators have the advantage that they offer an exact and uniform methodology for LS estimation of the LDV coefficient in a first order autoregressive model with or without exogenous regressors i.e. ARX(1).

However, no attempt has been made to accurately establish and/or compare the statistical properties among these estimators, or relative to those of the LS estimator when the LDV coefficient is restricted to realistic values. Neither has there been an attempt to
compare their performance in terms of their mean squared error (MSE) when various forms of the exogenous regressors are considered. Furthermore, only implicit confidence intervals have been given for the &lsquo
medianunbiased&rsquo
estimator. Explicit confidence bounds that are directly usable for inference are not available for either estimator. In this study a new estimator of the LDV coefficient is proposed
the &lsquo
most-probably-unbiased&rsquo
estimator. Its performance properties vis-a-vis the existing estimators are determined and compared when the parameter space of the LDV coefficient is restricted. In addition, the following new results are established: (1) an explicit computable form for the density of the LS estimator is derived for the first time and an efficient method for its numerical evaluation is proposed
(2) the exact bias, mean, median and mode of the distribution of the LS estimator are determined in three specifications of the ARX(1) model
(3) the exact variance and MSE of LS estimator is determined
(4) the standard error associated with the determination of same quantities when simulation rather than numerical integration method is used are established and the methods are compared in terms of computational time and effort
(5) an exact method of evaluating the density of the three estimators is described
(6) their exact bias, mean, variance and MSE are determined and analysed
and finally, (7) a method of obtaining the explicit exact confidence intervals from the distribution functions of the estimators is proposed.

The discussion and results show that the estimators are still biased in the usual sense: &lsquo
in expectation&rsquo
. However the bias is substantially reduced compared to that of the LS estimator. The findings are important in the specification of time-series regression models, point and interval estimation, decision theory, and simulation.

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40

Hussain, Zahir M. "Adaptive instantaneous frequency estimation: Techniques and algorithms." Thesis, Queensland University of Technology, 2002. https://eprints.qut.edu.au/36137/7/36137_Digitised%20Thesis.pdf.

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This thesis deals with the problem of the instantaneous frequency (IF) estimation of sinusoidal signals. This topic plays significant role in signal processing and communications. Depending on the type of the signal, two major approaches are considered. For IF estimation of single-tone or digitally-modulated sinusoidal signals (like frequency shift keying signals) the approach of digital phase-locked loops (DPLLs) is considered, and this is Part-I of this thesis. For FM signals the approach of time-frequency analysis is considered, and this is Part-II of the thesis. In part-I we have utilized sinusoidal DPLLs with non-uniform sampling scheme as this type is widely used in communication systems. The digital tanlock loop (DTL) has introduced significant advantages over other existing DPLLs. In the last 10 years many efforts have been made to improve DTL performance. However, this loop and all of its modifications utilizes Hilbert transformer (HT) to produce a signal-independent 90-degree phase-shifted version of the input signal. Hilbert transformer can be realized approximately using a finite impulse response (FIR) digital filter. This realization introduces further complexity in the loop in addition to approximations and frequency limitations on the input signal. We have tried to avoid practical difficulties associated with the conventional tanlock scheme while keeping its advantages. A time-delay is utilized in the tanlock scheme of DTL to produce a signal-dependent phase shift. This gave rise to the time-delay digital tanlock loop (TDTL). Fixed point theorems are used to analyze the behavior of the new loop. As such TDTL combines the two major approaches in DPLLs: the non-linear approach of sinusoidal DPLL based on fixed point analysis, and the linear tanlock approach based on the arctan phase detection. TDTL preserves the main advantages of the DTL despite its reduced structure. An application of TDTL in FSK demodulation is also considered. This idea of replacing HT by a time-delay may be of interest in other signal processing systems. Hence we have analyzed and compared the behaviors of the HT and the time-delay in the presence of additive Gaussian noise. Based on the above analysis, the behavior of the first and second-order TDTLs has been analyzed in additive Gaussian noise. Since DPLLs need time for locking, they are normally not efficient in tracking the continuously changing frequencies of non-stationary signals, i.e. signals with time-varying spectra. Nonstationary signals are of importance in synthetic and real life applications. An example is the frequency-modulated (FM) signals widely used in communication systems. Part-II of this thesis is dedicated for the IF estimation of non-stationary signals. For such signals the classical spectral techniques break down, due to the time-varying nature of their spectra, and more advanced techniques should be utilized. For the purpose of instantaneous frequency estimation of non-stationary signals there are two major approaches: parametric and non-parametric. We chose the non-parametric approach which is based on time-frequency analysis. This approach is computationally less expensive and more effective in dealing with multicomponent signals, which are the main aim of this part of the thesis. A time-frequency distribution (TFD) of a signal is a two-dimensional transformation of the signal to the time-frequency domain. Multicomponent signals can be identified by multiple energy peaks in the time-frequency domain. Many real life and synthetic signals are of multicomponent nature and there is little in the literature concerning IF estimation of such signals. This is why we have concentrated on multicomponent signals in Part-H. An adaptive algorithm for IF estimation using the quadratic time-frequency distributions has been analyzed. A class of time-frequency distributions that are more suitable for this purpose has been proposed. The kernels of this class are time-only or one-dimensional, rather than the time-lag (two-dimensional) kernels. Hence this class has been named as the T -class. If the parameters of these TFDs are properly chosen, they are more efficient than the existing fixed-kernel TFDs in terms of resolution (energy concentration around the IF) and artifacts reduction. The T-distributions has been used in the IF adaptive algorithm and proved to be efficient in tracking rapidly changing frequencies. They also enables direct amplitude estimation for the components of a multicomponent

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41

Ben, slimene Byrame. "Comportement asymptotique des solutions globales pour quelques problèmes paraboliques non linéaires singuliers." Thesis, Sorbonne Paris Cité, 2017. http://www.theses.fr/2017USPCD059/document.

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Dans cette thèse, nous étudions l’équation parabolique non linéaire ∂ t u = ∆u + a |x|⎺⥾ |u|ᵅ u, t > 0, x ∈ Rᴺ \ {0}, N ≥ 1, ⍺ ∈ R, α > 0, 0 < Ƴ < min(2,N) et avec une donnée initiale u(0) = φ. On établit l’existence et l’unicité locale dans Lq(Rᴺ) et dans Cₒ(Rᴺ). En particulier, la valeur q = N ⍺/(2 − γ) joue un rôle critique. Pour ⍺ > (2 − γ)/N, on montre l’existence de solutions auto-similaires globales avec données initiales φ(x) = ω(x) |x|−(2−γ)/⍺, où ω ∈ L∞(Rᴺ) homogène de degré 0 et ||ω||∞ est suffisamment petite. Nous montrons ainsi que si φ(x)∼ω(x) |x| ⎺(²⎺⥾)/⍺ pour |x| grande, alors la solution est globale et asymptotique dans L∞(Rᴺ) à une solution auto-similaire de l’équation non linéaire. Tandis que si φ(x)∼ω(x) |x| (x)|x|−σ pour des |x| grandes avec (2 − γ)/⍺ < σ < N, alors la solution est globale, mais elle est asymptotique dans L∞(Rᴺ) à eᵗ∆(ω(x) |x|−σ). L’équation avec un potentiel plus général, ∂ t u = ∆u + V(x) |u|ᵅ u, V(x) |x |⥾ ∈ L∞(Rᴺ), est également étudiée. En particulier, pour des données initiales φ(x)∼ω(x) |x| ⎺(²⎺⥾)/⍺, |x| grande, nous montrons que le comportement à grand temps est linéaire si V est à support compact au voisinage de l’origine, alors qu’il est non linéaire si V est à support compact au voisinage de l’infini. Nous étudions également le système non linéaire ∂ t u = ∆u + a |x|⎺⥾ |v|ᴾ⎺¹v, ∂ t v = ∆v + b |x|⎺ ᴾ |u|q⎺¹ u, t > 0, x ∈ Rᴺ \ {0}, N ≥ 1, a,b ∈ R, 0 < y < min(2,N)? 0 < p < min(2,N), p,q > 1. Sous des conditions sur les paramètres p, q, γ et ρ nous montrons l’existence et l’unicité de solutions globales avec données initiales petites par rapport à certaines normes. En particulier, on montre l’existence de solutions auto-similaires avec donnée initiale Φ = (φ₁, φ₂), où φ₁, φ₂ sont des données initiales homogènes. Nous montrons également que certaines solutions globales sont asymptotiquement auto-similaires. Comme deuxième objectif, nous considérons l’équation de la chaleur non linéaire ut = ∆u + |u|ᴾ⎺¹u - |u| q⎺¹u, avec t ≥ 0 et x ∈ Ω, la boule unité de Rᴺ, N ≥ 3, avec des conditions aux limites de Dirichlet. Soit h une solution stationnaire à symétrie radiale avec changement de signe de (E). On montre que la solution de (E) avec donnée initiale λh explose en temps fini si |λ − 1| > 0 est suffisamment petit et si 1 < q < p < Ps = N+2/N−2 et p suffisamment proche de Ps. Ceci prouve que l’ensemble des données initiales pour lesquelles la solution est globale n’est pas étoilé au voisinage de 0
In this thesis, we study the nonlinear parabolic equation ∂ t u = ∆u + a |x|⎺⥾ |u|ᵅ u, t > 0, x ∈ Rᴺ \ {0}, N ≥ 1, ⍺ ∈ R, α > 0, 0 < Ƴ < min(2,N) and with initial value u(0) = φ. We establish local well-posedness in Lq(Rᴺ) and in Cₒ(Rᴺ). In particular, the value q = N ⍺/(2 − γ) plays a critical role.For ⍺ > (2 − γ)/N, we show the existence of global self-similar solutions with initial values φ(x) = ω(x) |x|−(2−γ)/⍺, where ω ∈ L∞(Rᴺ) is homogeneous of degree 0 and ||ω||∞ is sufficiently small. We then prove that if φ(x)∼ω(x) |x| ⎺(²⎺⥾)/⍺ for |x| large, then the solution is global and is asymptotic in the L∞-norm to a self-similar solution of the nonlinear equation. While if φ(x)∼ω(x) |x| (x)|x|−σ for |x| large with (2 − γ)/α < σ < N, then the solution is global but is asymptotic in the L∞-norm toe t(ω(x) |x|−σ). The equation with more general potential, ∂ t u = ∆u + V(x) |u|ᵅ u, V(x) |x |⥾ ∈ L∞(Rᴺ), is also studied. In particular, for initial data φ(x)∼ω(x) |x| ⎺(²⎺⥾)/⍺, |x| large , we show that the large time behavior is linear if V is compactly supported near the origin, while it is nonlinear if V is compactly supported near infinity. we study also the nonlinear parabolic system ∂ t u = ∆u + a |x|⎺⥾ |v|ᴾ⎺¹v, ∂ t v = ∆v + b |x|⎺ ᴾ |u|q⎺¹ u, t > 0, x ∈ Rᴺ \ {0}, N ≥ 1, a,b ∈ R, 0 < y < min(2,N)? 0 < p < min(2,N), p,q > 1. Under conditions on the parameters p, q, γ and ρ we show the existence and uniqueness of global solutions for initial values small with respect of some norms. In particular, we show the existence of self-similar solutions with initial value Φ = (φ₁, φ₂), where φ₁, φ₂ are homogeneous initial data. We also prove that some global solutions are asymptotic for large time to self-similar solutions. As a second objective we consider the nonlinear heat equation ut = ∆u + |u|ᴾ⎺¹u - |u| q⎺¹u, where t ≥ 0 and x ∈ Ω, the unit ball of Rᴺ, N ≥ 3, with Dirichlet boundary conditions. Let h be a radially symmetric, sign-changing stationary solution of (E). We prove that the solution of (E) with initial value λ h blows up in finite time if |λ − 1| > 0 is sufficiently small and if 1 < q < p < Ps = N+2/N−2 and p sufficiently close to Ps. This proves that the set of initial data for which the solution is global is not star-shaped around 0

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42

Gao, Bing. "Clustering analysis for non-stationary time series /." 2006. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3242843.

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Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006.
Source: Dissertation Abstracts International, Volume: 67-11, Section: B, page: 6484. Adviser: Hernando Ombao. Includes bibliographical references (leaves 74-76) Available on microfilm from Pro Quest Information and Learning.

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43

CHEN, HUI-LONG, and 陳惠龍. "Studies on the non-gaussian and non-stationary time series." Thesis, 1992. http://ndltd.ncl.edu.tw/handle/19149235859596040085.

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44

Ojemakinde, Bukola Titilayo. "Support vector regression for non-stationary time series." 2006. http://etd.utk.edu/2006/OjemakindeBukola.pdf.

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45

Arkaah, Yaw Johnson. "On some aspects of non-stationary time series." Diss., 2000. http://hdl.handle.net/2263/25008.

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46

Llatas, Isabel. "Asymptotic inference for Nearly Non-Stationary Time Series." 1987. http://catalog.hathitrust.org/api/volumes/oclc/16102267.html.

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Анотація:

Thesis (Ph. D.)--University of Wisconsin--Madison, 1987.
Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 130-134).

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47

Cheng, Shao-Chieh, and 鄭劭傑. "Modal-Parameter Identification Using Non-Stationary Time Series." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/79768274889225665637.

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碩士
國立成功大學
航空太空工程學系碩博士班
95
This thesis studies Non-Stationary Time Series for the application of modal-parameter identification from non-stationary ambient vibration data. The original Time Series uses ARMA (Autoregressive Moving-Average) model, which contains autoregressive part and moving average part, to reconstruct the stationary ambient vibration data, and obtains modal parameter with autoregressive part of ARMA model. However, the original time series method is not applicable to non-stationary signal which is closer to natural environment. So we propose two ways to build a non-stationary time series model—by curve-fitting of amplitude and by introducing the basis function. We use this model to describe the non-stationary amplitude of data and we also apply it to modal-parameter identification from non-stationary ambient vibration data. Through numerical simulation, applicability and effectiveness of the proposed method of modal parameter identification from non-stationary ambient vibration data is demonstrated.

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48

Musselman, Marcus William. "Monitoring of biomedical systems using non-stationary signal analysis." 2013. http://hdl.handle.net/2152/23248.

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Monitoring of engineered systems consists of characterizing the normal behavior of the system and tracking departures from it. Techniques to monitor a system can be split into two classes based on their use of inputs and outputs of the system. Systems-based monitoring refers to the case when both inputs and outputs of a system are available and utilized. Conversely, symptomatic monitoring refers to the case when only outputs of the system are available. This thesis extended symptomatic and systems-based monitoring of biomedical systems via the use of non-stationary signal processing and advanced monitoring methods. Monitoring of various systems of the human body is encumbered by several key hurdles. First, current biomedical knowledge may not fully comprehend the extent of inputs and outputs of a particular system. In addition, regardless of current knowledge, inputs may not be accessible and outputs may be, at best, indirect measurements of the underlying biological process. Finally, even if inputs and outputs are measurable, their relationship may be highly nonlinear and convoluted. These hurdles require the use of advanced signal processing and monitoring approaches. Regardless of the pursuit of symptomatic or system-based monitoring, the aforementioned hurdles can be partially overcome by using non-stationary signal analysis to reveal the way frequency content of biomedical signals change over time. Furthermore, the use of advanced classification and monitoring methods facilitated reliable differentiation between various conditions of the monitored system based on the information from non-stationary signal analysis. The human brain was targeted for advancement of symptomatic monitoring, as it is a system responding to a plethora internal and external stimuli. The complexity of the brain makes it unfeasible to realize system-based monitoring to utilize all the relevant inputs and outputs for the brain. Further, measurement of brain activity (outputs), in the indirect form of electroencephalogram (EEG), remains a workhorse of brain disorder diagnosis. In this thesis, advanced signal processing and pattern recognition methods are employed to devise and study an epilepsy detection and localization algorithm that outperforms those reported in literature. This thesis also extended systems-based monitoring of human biomedical systems via advanced input-output modeling and sophisticated monitoring techniques based on the information from non-stationary signal analysis. Explorations of system-based monitoring in the NMS system were driven by the fact that joint velocities and torques can be seen NMS responses to electrical inputs provided by the central nervous system (CNS) and the electromyograph (EMG) provides an indirect measurement of CNS excitations delivered to the muscles. Thus, both inputs and outputs of this system are more or less available and one can approach its monitoring via the use of system-based approaches.
text

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49

Du, Plessis Marthinus Christoffel. "Non-stationary signal classification for radar transmitter identification." Diss., 2010. http://hdl.handle.net/2263/27843.

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The radar transmitter identification problem involves the identification of a specific radar transmitter based on a received pulse. The radar transmitters are of identical make and model. This makes the problem challenging since the differences between radars of identical make and model will be solely due to component tolerances and variation. Radar pulses also vary in time and frequency which means that the problem is non-stationary. Because of this fact, time-frequency representations such as shift-invariant quadratic time-frequency representations (Cohen’s class) and wavelets were used. A model for a radar transmitter was developed. This consisted of an analytical solution to a pulse-forming network and a linear model of an oscillator. Three signal classification algorithms were developed. A signal classifier was developed that used a radially Gaussian Cohen’s class transform. This time-frequency representation was refined to increase the classification accuracy. The classification was performed with a support vector machine classifier. The second signal classifier used a wavelet packet transform to calculate the feature values. The classification was performed using a support vector machine. The third signal classifier also used the wavelet packet transform to calculate the feature values but used a Universum type classifier for classification. This classifier uses signals from the same domain to increase the classification accuracy. The classifiers were compared against each other on a cubic and exponential chirp test problem and the radar transmitter model. The classifier based on the Cohen’s class transform achieved the best classification accuracy. The classifier based on the wavelet packet transform achieved excellent results on an Electroencephalography (EEG) test dataset. The complexity of the wavelet packet classifier is significantly lower than the Cohen’s class classifier. Copyright
Dissertation (MEng)--University of Pretoria, 2010.
Electrical, Electronic and Computer Engineering
unrestricted

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50

Muscolino, G., and Alessandro Palmeri. "Peak response of non-linear oscillators under stationary white noise." 2007. http://hdl.handle.net/10454/601.

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The use of the Advanced Censored Closure (ACC) technique, recently proposed by the authors for predicting the peak response of linear structures vibrating under random processes, is extended to the case of non-linear oscillators driven by stationary white noise. The proposed approach requires the knowledge of mean upcrossing rate and spectral bandwidth of the response process, which in this paper are estimated through the Stochastic Averaging method. Numerical applications to oscillators with non-linear stiffness and damping are included, and the results are compared with those given by Monte Carlo Simulation and by other approximate formulations available in the literature.

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