Дисертації з теми "Non-Autoregressive"
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Ознайомтеся з топ-33 дисертацій для дослідження на тему "Non-Autoregressive".
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Clayton, Maya. "Econometric forecasting of financial assets using non-linear smooth transition autoregressive models." Thesis, University of St Andrews, 2011. http://hdl.handle.net/10023/1898.
Повний текст джерелаLiu, Ka-yee. "Bayes and empirical Bayes estimation for the panel threshold autoregressive model and non-Gaussian time series." Click to view the E-thesis via HKUTO, 2005. http://sunzi.lib.hku.hk/hkuto/record/B30706166.
Повний текст джерелаLiu, Ka-yee, and 廖家怡. "Bayes and empirical Bayes estimation for the panel threshold autoregressive model and non-Gaussian time series." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2005. http://hub.hku.hk/bib/B30706166.
Повний текст джерелаNyman, Nick, and Smura Michel Postigo. "Examining how unforeseen events affect accuracy and recovery of a non-linear autoregressive neural network in stock market prognoses." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186435.
Повний текст джерелаDenna studie undersöker hur ett icke-linjärt autoregressivt neuronnät för aktiemarknadsprognoser påverkas av oväntade händelser. Studien ämnar finna återhämtningsperioden för nätverket efter en händelse, och ta reda på om den initiala påverkan av händelsen påverkar återhämtningen. Tester av endagsprognosers avvikelse från det verkliga värdet genomförs på fem verkliga aktier och fyra skapade dataset som exkluderar den omgivande variationen från aktiemarknaden. Dessa simulerade set isolerar därmed specifika typer av händelser. Studien drar slutsatsen att storleken av händelsen har försumbar betydelse på återhämtningstiden och att plötsliga händelser tillåter återhämtning på några dagar oavsett händelsens ursprungliga storlek eller förändring av prisutvecklingshastighet. Däremot förlänger utdragna händelser återhämtningstiden. Likaså påverkar efterskalv eller kvarvarande instabilitet i prisutvecklingen tillförlitlighet och återhämtningstid avsevärt.
Krisztin, Tamás. "Semi-parametric spatial autoregressive models in freight generation modeling." Elsevier, 2018. https://publish.fid-move.qucosa.de/id/qucosa%3A72336.
Повний текст джерелаWang, Yuefeng. "Essays on modelling house prices." Thesis, Brunel University, 2018. http://bura.brunel.ac.uk/handle/2438/16242.
Повний текст джерелаCugliari, Jairo. "Prévision non paramétrique de processus à valeurs fonctionnelles : application à la consommation d’électricité." Thesis, Paris 11, 2011. http://www.theses.fr/2011PA112234/document.
Повний текст джерелаThis thesis addresses the problem of predicting a functional valued stochastic process. We first explore the model proposed by Antoniadis et al. (2006) in the context of a practical application -the french electrical power demand- where the hypothesis of stationarity may fail. The departure from stationarity is twofold: an evolving mean level and the existence of groupsthat may be seen as classes of stationarity.We explore some corrections that enhance the prediction performance. The corrections aim to take into account the presence of these nonstationary features. In particular, to handle the existence of groups, we constraint the model to use only the data that belongs to the same group of the last available data. If one knows the grouping, a simple post-treatment suffices to obtain better prediction performances.If the grouping is unknown, we propose it from data using clustering analysis. The infinite dimension of the not necessarily stationary trajectories have to be taken into account by the clustering algorithm. We propose two strategies for this, both based on wavelet transforms. The first one uses a feature extraction approach through the Discrete Wavelet Transform combined with a feature selection algorithm to select the significant features to be used in a classical clustering algorithm. The second approach clusters directly the functions by means of a dissimilarity measure of the Continuous Wavelet spectra.The third part of thesis is dedicated to explore an alternative prediction model that incorporates exogenous information. For this purpose we use the framework given by the Autoregressive Hilbertian processes. We propose a new class of processes that we call Conditional Autoregressive Hilbertian (carh) and develop the equivalent of projection and resolvent classes of estimators to predict such processes
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.
Повний текст джерелаCaron, Nathalie. "Approches alternatives d'une théorie non informative des tests bayésiens." Rouen, 1994. http://www.theses.fr/1994ROUES028.
Повний текст джерелаKorale, Asoka Jeevaka Maligaspe. "Non-stationary adaptive signal prediction with error bounds." Thesis, Imperial College London, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.326258.
Повний текст джерелаArotiba, Gbenga Joseph. "Pricing American Style Employee Stock Options having GARCH Effects." Thesis, University of the Western Cape, 2010. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_3057_1298615964.
Повний текст джерелаWe investigate some simulation-based approaches for the valuing of the employee stock options. The mathematical models that deal with valuation of such options include the work of Jennergren and Naeslund [L.P Jennergren and B. Naeslund, A comment on valuation of executive stock options and the FASB proposal, Accounting Review 68 (1993) 179-183]. They used the Black and Scholes [F. Black and M. Scholes, The pricing of options and corporate liabilities, Journal of Political Economy 81(1973) 637-659] and extended partial differential equation for an option that includes the early exercise. Some other major relevant works to this mini thesis are Hemmer et al. [T Hemmer, S. Matsunaga and T Shevlin, The influence of risk diversification on the early exercise of employee stock options by executive officers, Journal of Accounting and Economics 21(1) (1996) 45-68] and Baril et al. [C. Baril, L. Betancourt, J. Briggs, Valuing employee stock options under SFAS 123 R using the Black-Scholes-Merton and lattice model approaches, Journal of Accounting Education 25 (1-2) (2007) 88-101]. The underlying assets are studied under the GARCH (generalized autoregressive conditional heteroskedasticity) effects. Particular emphasis is made on the American style employee stock options.
Jin, Fei. "Essays in Spatial Econometrics: Estimation, Specification Test and the Bootstrap." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1365612737.
Повний текст джерелаSànchez, 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.
Повний текст джерелаThis 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
Gomes, Leonaldo da Silva. "Redes Neurais Aplicadas à InferÃncia dos Sinais de Controle de Dosagem de Coagulantes em uma ETA por FiltraÃÃo RÃpida." Universidade Federal do CearÃ, 2012. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=8105.
Повний текст джерелаConsidering the importance of the chemical coagulation control for the water treatment by direct filtration, this work proposes the application of artificial neural networks for inference of dosage control signals of principal and auxiliary coagulant, in the chemical coagulation process in a water treatment plant by direct filtration. To that end, was made a comparative analysis of the application of models based on neural networks, such as: Focused Time Lagged Feedforward Network (FTLFN); Distributed Time Lagged Feedforward Network (DTLFN); Elman Recurrent Network (ERN) and Non-linear Autoregressive with exogenous inputs (NARX). From the comparative analysis, the model based on NARX networks showed better results, demonstrating the potential of the model for use in real cases, which will contribute to the viability of projects of this nature in small size water treatment plants.
Dupré, la Tour Tom. "Nonlinear models for neurophysiological time series." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLT018/document.
Повний текст джерелаIn neurophysiological time series, strong neural oscillations are observed in the mammalian brain, and the natural processing tools are thus centered on narrow-band linear filtering.As this approach is too reductive, we propose new methods to represent these signals.We first focus on the study of phase-amplitude coupling (PAC), which consists in an amplitude modulation of a high frequency band, time-locked with a specific phase of a slow neural oscillation.We propose to use driven autoregressive models (DAR), to capture PAC in a probabilistic model. Giving a proper model to the signal enables model selection by using the likelihood of the model, which constitutes a major improvement in PAC estimation.%We first present different parametrization of DAR models, with fast inference algorithms and stability discussions.Then, we present how to use DAR models for PAC analysis, demonstrating the advantage of the model-based approach on three empirical datasets.Then, we explore different extensions to DAR models, estimating the driving signal from the data, PAC in multivariate signals, or spectro-temporal receptive fields.Finally, we also propose to adapt convolutional sparse coding (CSC) models for neurophysiological time-series, extending them to heavy-tail noise distribution and multivariate decompositions. We develop efficient inference algorithms for each formulation, and show that we obtain rich unsupervised signal representations
Lopez, Marcano Juan L. "Classification of ADHD and non-ADHD Using AR Models and Machine Learning Algorithms." Thesis, Virginia Tech, 2016. http://hdl.handle.net/10919/73688.
Повний текст джерелаMaster of Science
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.
Повний текст джерелаIn 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.
Rabah-Romdhane, Zohra. "Etudes sur le cycle économique. Une approche par les modèles à changements de régime." Thesis, Université de Lorraine, 2013. http://www.theses.fr/2013LORR0322.
Повний текст джерелаThe severity of the Great Recession has renewed interest in the analysis of business cycles. Our thesis pertains to this revival of attention for the study of cyclical fluctuations. After reviewing the regime-switching models in Chapter one, the following chapter suggests a chronology of the classical business cycle in French economy for the 1970-2009 period. To that end, three dating methodologies are used: the rule of thumb of two consecutive quarters of negative growth, the non-parametric approach of Bry and Boschan (1971), and the Markov-switching approach of Hamilton (1989). The results show that,omitted structural breaks may hinder the Markov-switching approach to capture business-cycle fluctuations. However, when such breaks are allowed for, the timing of the French recessions provided by the Markov-switching model closely matches those derived by the rule-based approaches.Chapter 3 performs a nonlinearity analysis inMarkov-switching modelling using a set of non-standard tests. Monte Carlo analysis reveals that a recently test proposed by Carrasco, Hu, and Ploberger (2013) for Markov switching has low power for empirically-relevant data generating processes when allowing for serial correlation under the null. By contrast, a parametric bootstrap likelihood ratio (LR) test of Markov switching has higher power in the same setting, providing stronger support for nonlinearity in quarterly French and U.S. real GDP. When testing for Markov switching in mean or intercept of an autoregressive process, it is important to allow for serial correlation under the null hypothesis of linearity.Otherwise, a rejection of linearity could merely reflect misspecification of the persistence properties of the data, rather than any inherent nonlinearity.Chapter 4 examines whether controlling for structural breaks improves the forecasting performance of the Markov-switching models, as compared to their linear counterparts.The approach considered to answer this issue is to combined forecasts across different estimation windows. The outcome of applying such an approach shows that, including data from periods preceding structural breaks and particularly the "Great Moderation" improves upon forecasts based on data drawn exclusively from these episodes. Accordingly, Markov-switching models forecast the probability of events such as the Great Recession more accurately than their linear counterparts.The general conclusions summarize the main results of the thesis and, suggest several directions for future research
Olivier, Adelaïde. "Analyse statistique des modèles de croissance-fragmentation." Thesis, Paris 9, 2015. http://www.theses.fr/2015PA090047/document.
Повний текст джерелаThis work is concerned with growth-fragmentation models, implemented for investigating the growth of a population of cells which divide according to an unknown splitting rate, depending on a structuring variable – age and size being the two paradigmatic examples. The mathematical framework includes statistics of processes, nonparametric estimations and analysis of partial differential equations. The three objectives of this work are the following : get a nonparametric estimate of the division rate (as a function of age or size) for different observation schemes (genealogical or continuous) ; to study the transmission of a biological feature from one cell to an other and study the feature of one typical cell ; to compare different populations of cells through their Malthus parameter, which governs the global growth (when introducing variability in the growth rate among cells for instance)
Relvas, Carlos Eduardo Martins. "Modelos parcialmente lineares com erros simétricos autoregressivos de primeira ordem." Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-28052013-182956/.
Повний текст джерелаIn this master dissertation, we present the symmetric partially linear models with AR(1) errors that generalize the normal partially linear models to contain autocorrelated errors AR(1) following a symmetric distribution instead of the normal distribution. Among the symmetric distributions, we can consider heavier tails than the normal ones, controlling the kurtosis and down-weighting outlying observations in the estimation process. The parameter estimation is made through the penalized likelihood by using score functions and the expected Fisher information. We derive these functions in this work. The effective degrees of freedom and asymptotic results are also presented as well as the residual analysis, highlighting the normal curvature of local influence under different perturbation schemes. An application with real data is given for illustration.
Van, Heerden Petrus Marthinus Stephanus. "The relationship between the forward– and the realized spot exchange rate in South Africa / Petrus Marthinus Stephanus van Heerden." Thesis, North-West University, 2010. http://hdl.handle.net/10394/4511.
Повний текст джерелаThesis (Ph.D. (Risk management))--North-West University, Potchefstroom Campus, 2011.
Kahaei, Mohammad Hossein. "Performance analysis of adaptive lattice filters for FM signals and alpha-stable processes." Thesis, Queensland University of Technology, 1998. https://eprints.qut.edu.au/36044/7/36044_Digitised_Thesis.pdf.
Повний текст джерелаPetitjean, Julien. "Contributions au traitement spatio-temporel fondé sur un modèle autorégressif vectoriel des interférences pour améliorer la détection de petites cibles lentes dans un environnement de fouillis hétérogène Gaussien et non Gaussien." Thesis, Bordeaux 1, 2010. http://www.theses.fr/2010BOR14157/document.
Повний текст джерелаThis dissertation deals with space-time adaptive processing in the radar’s field. To improve the detection’s performances, this approach consists in maximizing the ratio between the target’s power and the interference’s one, i.e. the thermal noise and the clutter. Several variants of its algorithm exist, one of them is based on multichannel autoregressive modelling of interferences. Its main problem lies in the estimation of autoregressive matrices with training data and guides our research’s work. Especially, our contribution is twofold.On the one hand, when thermal noise is considered negligible, autoregressive matrices are estimated with fixed point method. Thus, the algorithm is robust against non-gaussian clutter.On the other hand, a new modelling of interferences is proposed. The clutter and thermal noise are separated : the clutter is considered as a multichannel autoregressive process which is Gaussian and disturbed by the white thermal noise. Thus, new estimation’s algorithms are developed. The first one is a blind estimation based on errors in variable methods. Then, recursive approaches are proposed and used extension of Kalman filter : the extended Kalman filter and the Sigma Point Kalman filter (UKF and CDKF), and the H∞ filter. A comparative study on synthetic and real data with Gausian and non Gaussian clutter is carried out to show the relevance of the different algorithms about detection’s probability
Ahmed, Mohamed Salem. "Contribution à la statistique spatiale et l'analyse de données fonctionnelles." Thesis, Lille 3, 2017. http://www.theses.fr/2017LIL30047/document.
Повний текст джерелаThis thesis is about statistical inference for spatial and/or functional data. Indeed, weare interested in estimation of unknown parameters of some models from random or nonrandom(stratified) samples composed of independent or spatially dependent variables.The specificity of the proposed methods lies in the fact that they take into considerationthe considered sample nature (stratified or spatial sample).We begin by studying data valued in a space of infinite dimension or so-called ”functionaldata”. First, we study a functional binary choice model explored in a case-controlor choice-based sample design context. The specificity of this study is that the proposedmethod takes into account the sampling scheme. We describe a conditional likelihoodfunction under the sampling distribution and a reduction of dimension strategy to definea feasible conditional maximum likelihood estimator of the model. Asymptotic propertiesof the proposed estimates as well as their application to simulated and real data are given.Secondly, we explore a functional linear autoregressive spatial model whose particularityis on the functional nature of the explanatory variable and the structure of the spatialdependence. The estimation procedure consists of reducing the infinite dimension of thefunctional variable and maximizing a quasi-likelihood function. We establish the consistencyand asymptotic normality of the estimator. The usefulness of the methodology isillustrated via simulations and an application to some real data.In the second part of the thesis, we address some estimation and prediction problemsof real random spatial variables. We start by generalizing the k-nearest neighbors method,namely k-NN, to predict a spatial process at non-observed locations using some covariates.The specificity of the proposed k-NN predictor lies in the fact that it is flexible and allowsa number of heterogeneity in the covariate. We establish the almost complete convergencewith rates of the spatial predictor whose performance is ensured by an application oversimulated and environmental data. In addition, we generalize the partially linear probitmodel of independent data to the spatial case. We use a linear process for disturbancesallowing various spatial dependencies and propose a semiparametric estimation approachbased on weighted likelihood and generalized method of moments methods. We establishthe consistency and asymptotic distribution of the proposed estimators and investigate thefinite sample performance of the estimators on simulated data. We end by an applicationof spatial binary choice models to identify UADT (Upper aerodigestive tract) cancer riskfactors in the north region of France which displays the highest rates of such cancerincidence and mortality of the country
Shakibi, Babak. "Resolution Enhancement of Ultrasonic Signals using Autoregressive Spectral Extrapolation." Thesis, 2011. http://hdl.handle.net/1807/29619.
Повний текст джерелаNunes, Diana Catherina Manaig. "Modelling granules size distribution produced on a continuous manufacturating line with non-linear autoregressive artificial neural networks." Master's thesis, 2018. http://hdl.handle.net/10451/40066.
Повний текст джерелаParticle size is a critical quality parameter in several pharmaceutical unit operations. An adequate particle size distribution is essential to ensure optimal manufacturability which, in turn, has an important impact on the safety, efficacy and quality of the end product. Thus, the monitoring and control of the particle size via in-process size measurements is crucial to the pharmaceutical industry. Currently, a wide range of techniques are available for the determination of particle size distribution, however a technique that enables relevant real-time process data is highly preferable, as a better understanding and control over the process is offered. The pharmaceutical industry follows the “technology-push model” as it depends on scientific and technological advances. Hence, optimization of product monitoring technologies for drug products have been receiving more attention as it helps to increase profitability. An increasing interest in the usage of virtual instruments as an alternative to physical instruments has arisen in recent years. A software sensor utilizes information collected from a process operation to estimate values of some property of interest, typically difficult to measure experimentally. One of the most significant benefits of the computational approach is the possibility to adapt the measuring system through several optimization solutions. The present thesis focuses on the development of a mathematical dynamic model capable of predicting particle size distribution in-real time. For this purpose, multivariate data coming from univariate sensors placed in multiple locations of the continuous production line, ConsiGmaTM-25, was utilized to determine the size distribution (d50) of granules evaluated at a specific site within the line. The ConsiGmaTM-25 system is a continuous granulation line developed by GEA Pharma. It consists of three modules: a continuous twin-screw granulation module, a six-segmented cell fluid bed dryer and a product control unit. In the continuous granulation module, granules are produced inside the twin-screw granulator via mixing of the powder and the granulation liquid (water) fed into the granulation barrel. Once finalized the granulation operation, the produced granules are then pneumatically transferred to the fluid bed dryer module. In the dryer module, the granules are relocated to one specific dryer cell, where drying is performed for a pre-defined period of time. The dry granules are formerly transported to the product control hopper with an integrated mill situated in the product control unit. The granules are milled, and the resulting product is gravitationally discharged and can undergo further processing steps, such as blending, tableting and coating. The size distribution (d50) of the granules to be determined in this work were assessed inside dryer cell no.4, located at the dryer module. The size distribution was measured every ten seconds by a focused beam reflectance measurement technique. A non-linear autoregressive with exogenous inputs network was developed to achieve accurate predictions of granules size distribution values. The development of the predictive model consisted of the implementation of an optimization strategy in terms of topology, inputs, delays and training methodology. The network was trained against the d50 obtained from particle size distribution collected in-situ by the focused beam reflectance measurement technique mentioned above. The model presented the ability to predict the d50 value from the beginning to the end of the several drying cycles. The accuracy of the artificial neural network was determined by a root mean squared error of prediction of 6.9%, which demonstrated the capability to produce close results to the experimental data of the cycles/runs included on the testing set. The predictive ability of the neural network, however, could not be extended to drying cycle that presented irregular fluctuations. Due to the importance of the precise monitoring of the size distribution within pharmaceutical operations, a future adjustment of the optimization strategy is of great interest. In the future, a higher number of experimental runs/cycles can be used during the training process to enable the network to identify and predict more easily atypical cases. In addition, a more realistic optimization strategy could be performed for all process parameters in simultaneous through the implementation of a genetic algorithm, for example. Changes in terms of network topology can also be considered.
O tamanho de partícula é um parâmetro crítico de qualidade em diversas operações unitárias da indústria farmacêutica. Uma distribuição de tamanho de partícula adequada é essencial para garantir condições ideais de fabrico, o que por sua vez, possui um impacto significativo na segurança, eficácia e qualidade do produto final. Deste modo, a monitorização e controlo do tamanho de partícula através de medições efetuadas durante o processo são consideradas cruciais para a indústria. Atualmente, uma ampla gama de técnicas encontra-se disponível para a determinação da distribuição de tamanho de partícula. Contudo, uma técnica que permita a obtenção de dados relevantes em tempo real é altamente preferível, visto que um melhor entendimento e controlo sobre o processo é obtido. A indústria farmacêutica encontra-se altamente dependente de avanços científicos e tecnológicos. Nos últimos anos, um interesse crescente no uso de instrumentos virtuais como alternativa à instrumentalização física na monitorização de produto é evidente. Um sensor virtual faz uso da informação contida num determinado conjunto de dados para efetuar medições adequadas de uma propriedade de interesse. Uma das vantagens mais importantes desta abordagem computacional corresponde à possibilidade de adaptação do sistema de medição, recorrendo a variados métodos de otimização. A presente tese encontra-se focada no desenvolvimento de um modelo matemático dinâmico capaz de prever a distribuição de tamanho de partícula em tempo real. Para o efeito, dados multivariados gerados, a cada segundo, por sensores localizados em múltiplos locais da linha de produção contínua, ConsiGmaTM-25, foram utilizados para determinar a distribuição de tamanho (d50) de grânulos avaliada num ponto específico da linha. O sistema ConsiGmaTM-25 trata-se de uma linha contínua de produção de grânulos, que pode ser dividida, essencialmente, em três módulos principais: granulador contínuo, secador de leito fluido e unidade de acondicionamento de produto. No módulo de granulação, ocorre a produção de grânulos através da mistura de pó e água (líquido de granulação). Uma vez finalizada a operação unitária, os grânulos produzidos são pneumaticamente transferidos para o secador de leito fluido. Neste local, os grânulos são introduzidos numa das seis células de secagem, onde ocorre o processo de secagem durante um período de tempo pré-definido. Os grânulos secos resultantes são, de seguida, transferidos para a unidade de acondicionamento de produto, integrado por um moinho, responsável pela operação de moagem. O material moído é gravitacionalmente descarregado e pode ser novamente processado através de operações como a mistura, compressão ou revestimento. A distribuição de tamanho (d50) dos grânulos a ser determinada neste trabalho foi medida, a cada dez segundos, através da técnica de reflectância por um feixe de luz focalizado. Um total de dezasseis corridas realizadas no mês de agosto foram utilizadas neste trabalho. Para cada corrida, dados relativos a parâmetros de processo tais como, pressões, temperaturas, fluxos de ar, entre outros, bem como, a distribuição do tamanho (d50) dos grânulos foram disponibilizados. Com base na discrepância temporal verificada entre os dados de processo e os valores de distribuição de tamanho (d50) dos grânulos, diversas etapas de processamento foi executadas. O processamento de dados foi realizado, essencialmente, em três fases distintas: alinhamento, filtragem e organização/fragmentação. Uma vez finalizado o processamento, os dados foram utilizados no desenvolvimento do modelo preditivo (rede neural). Uma rede neuronal não-linear autorregressiva com três entradas exógenas foi desenvolvida para realizar previsões da distribuição de tamanho (d50) dos grânulos. O desenvolvimento do modelo preditivo consistiu na implementação de uma estratégia de otimização em termos de topologia, atrasos, dados de entrada, seleção de corridas e metodologia de treino. Para cada variável de processo (entrada), um atraso foi assinalado com base em pressupostos fundamentados por estudos relativos ao tempo de residência dos três módulos da linha contínua. Os dados de entrada foram definidos com base no resultado de um modelo matemático desenvolvido para designar o conjunto de variáveis para o qual se observava um menor erro médio quadrático de previsão da propriedade de interesse, d50. De forma a possibilitar o treino da rede, os dados fragmentados foram divididos em dois principais conjuntos: treino e teste. A rede foi treinada e validada com dados de treino, sendo os dados de teste seguidamente utilizados para avaliar a capacidade preditiva do modelo otimizado. O modelo apresentou a capacidade de prever o valor de d50 ao longo dos vários ciclos de secagem. A precisão da rede neural foi determinada por um valor de erro médio quadrático de previsão de 6,9%, demonstrando sua capacidade de produzir resultados próximos aos dados experimentais incluídos no conjunto de teste. A capacidade preditiva da rede neural, no entanto, não foi capaz de abranger casos atípicos. Considerando a importância de uma monitorização precisa da distribuição de tamanho nas operações farmacêuticas, uma futura alteração na estratégia de otimização implementada é altamente aconselhável. No futuro, o uso de um número mais elevado de ciclos/corridas de secagem durante o processo de treino da rede poderá permitir que esta seja capaz de identificar e prever com maior facilidade casos atípicos. Adicionalmente, uma abordagem mais realista da estratégia de otimização poderá ser executada para todas os parâmetros de processo em simultâneo através da implementação de um algoritmo genético. Ainda, alterações na topologia da rede poderão ser também consideradas.
LEE, SONG-SIOU, and 李松修. "The Non-linear Adjustment of Taiwan Stock Index Returns and Macroeconomic Variables: Using Smooth Transition Autoregressive STAR-ANSTGARCH Model." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/54661775463446713392.
Повний текст джерела國立臺北大學
企業管理學系
100
This paper attempts to investigate how the TAIEX stock returns were adjusted through time path with and without macroeconomic variables using monthly sample data from June 1996 to June 2011. By employing smooth transition autoregressive model(STAR) and ANSTGARCH model to depict asymmetric and nonlinear behaviors of the TAIEX returns, the findings are listed below. 1. TAIEX returns adjust in non-linear path. 2. The LSTAR model is better than the ESTAR model in measuring TAIEX returns adjustment process. 3. The STAR-ANSTGARCH model could properly estimate the asymmetric and nonlinear behavior of conditional mean and variance of TAIEX returns. 4. The Taiwan Coincident indicator could effectively explain the conditional mean and conditional variance of TAIEX returns.
Huang, Hsiao-Yun, and 黃筱雲. "A Study of Non-linear Co-relation between Mutual Fund’s Holding Rate and Stock Price under Different Stock Market Conditions - Panel Threshold Autoregressive Model Approach." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/57443962123975402196.
Повний текст джерела淡江大學
財務金融學系碩士在職專班
95
The purpose of this paper is to study the “threshold effect” between the proportion of mutual fund’s holding on electronic company and its stock prices by applying the panel threshold auto-co-relation model. It examines if there is one or multiple optimal thresholds to have reverse effects between the proportions of the stock holding and the stock prices. Applying the empirical results, we can conclude that investors can increase their stock holding when the proportion of the mutual fund’s holding starts increasing from a very low level. Even the stock prices fall in the beginning when the proportion of the mutual fund’s start increasing in a falling market. However, the stock prices eventually go up higher from a long-term perspective. On the other hand, it is less indicative that the stock prices go up or down when the market is consolidating according to the empirical results. Finally, the transaction volume is positively related to the stock prices and therefore is a useful indicator. On the other hand, the Nasdaq index or the exchange rate is less useful.
Zhang, Kai. "Does purchasing power parity hold between European countries? : investigation using non-linear STAR model." Master's thesis, 2012. http://hdl.handle.net/10400.14/15429.
Повний текст джерелаNgwangwa, Harry Magadhlela. "Road surface profile monitoring based on vehicle response and artificial neural network simulation." Thesis, 2015. http://hdl.handle.net/2263/43788.
Повний текст джерелаThesis (PhD)--University of Pretoria, 2015.
Mechanical and Aeronautical Engineering
Unrestricted
Costa, Sofia Martinho de Almeida. "Cross-sectional modeling of bank deposits." Master's thesis, 2019. http://hdl.handle.net/10362/91822.
Повний текст джерелаGrégoire, Gabrielle. "Sur les modèles non-linéaires autorégressifs à transition lisse et le calcul de leurs prévisions." Thèse, 2019. http://hdl.handle.net/1866/22550.
Повний текст джерелаLemyre, Gabriel. "Modèles de Markov à variables latentes : matrice de transition non-homogène et reformulation hiérarchique." Thesis, 2021. http://hdl.handle.net/1866/25476.
Повний текст джерелаThis master’s thesis is centered on the Hidden Markov Models, a family of models in which an unobserved Markov chain dictactes the behaviour of an observable stochastic process through which a noisy version of the latent chain is observed. These bivariate stochastic processes that can be seen as a natural generalization of mixture models have shown their ability to capture the varying dynamics of many time series and, more specifically in finance, to reproduce the stylized facts of financial returns. In particular, we are interested in discrete-time Markov chains with finite state spaces, with the objective of studying the contribution of their hierarchical formulations and the relaxation of the homogeneity hypothesis for the transition matrix to the quality of the fit and predictions, as well as the capacity to reproduce the stylized facts. We therefore present two hierarchical structures, the first allowing for new interpretations of the relationships between states of the chain, and the second allowing for a more parsimonious parameterization of the transition matrix. We also present three non-homogeneous models, two of which have transition probabilities dependent on observed explanatory variables, and the third in which the probabilities depend on another latent variable. We first analyze the goodness of fit and the predictive power of our models on the series of log returns of the S&P 500 and the exchange rate between canadian and american currencies (CADUSD). We also illustrate their capacity to reproduce the stylized facts, and present interpretations of the estimated parameters for the hierarchical and non-homogeneous models. In general, our results seem to confirm the contribution of hierarchical and non-homogeneous models to these measures of performance. In particular, these results seem to suggest that the incorporation of non-homogeneous dynamics to a hierarchical structure may allow for a more faithful reproduction of the stylized facts—even the slow decay of the autocorrelation functions of squared and absolute returns—and better predictive power, while still allowing for the interpretation of the estimated parameters.