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Dissertations / Theses on the topic 'Dynamic series'
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1
Haas, Markus. "Dynamic mixture models for financial time series /." Berlin : Pro Business, 2004. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=012999049&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.
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VASSALLO, Danilo. "Dynamic models for financial and sentiment time series." Doctoral thesis, Scuola Normale Superiore, 2022. http://hdl.handle.net/11384/109584.
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Zhang, Guangjian. "Bootstrap procedures for dynamic factor analysis." Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1153782819.
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Zhou, Pu. "A dynamic approximate representation scheme for streaming time series." Connect to thesis, 2009. http://repository.unimelb.edu.au/10187/6766.
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The huge volume of time series data generated in many applications poses new challenges in the techniques of data storage, transmission, and computation. Further more, when the time series are in the form of streaming data, new problems emerge and new techniques are required because of the streaming characteristics, e.g. high volume, high speed and continuous flowing. Approximate representation is one of the most efficient and effective solutions to address the large-volume-high-speed problem. In this thesis, we propose a dynamic representation scheme for streaming time series. Existing methods use a unitary function form for the entire approximation task. In contrast, our method adopts a set of function candidates such as linear function, polynomial function(degree ≥ 2), and exponential function. We provide a novel segmenting strategy to generate subsequences and dynamically choose candidate functions to approximate the subsequences.<br>Since we are dealing with streaming time series, the segmenting points and the corresponding approximate functions are incrementally produced. For a certain function form, we use a buffer window to find the local farthest possible segmenting point under a user specified error tolerance threshold. To achieve this goal, we define a feasible space for the coefficients of the function and show that we can indirectly find the local best segmenting point by the calculation in the coefficient space. Given the error tolerance threshold, the candidate function representing more information by unit parameter is chosen as the approximate function. Therefore, our representation scheme is more flexible and compact. We provide two dynamic algorithms, PLQS and PLQES, which involve two and three candidate functions, respectively. We also present the general strategy of function selection when more candidate functions are considered. In the experimental test, we examine the effectiveness of our algorithms with synthetic and real time series data sets. We compare our method with the piecewise linear approximation method and the experimental results demonstrate the evident superiority of our dynamic approach under the same error tolerance threshold.
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Jemwa, Gorden Takawadiyi. "Multivariate nonlinear time series analysis of dynamic process systems." Thesis, Stellenbosch : University of Stellenbosch, 2003. http://hdl.handle.net/10019.1/16339.
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Thesis (MScIng)--University of Stellenbosch, 2003.<br>ENGLISH ABSTRACT: Physical systems encountered in process engineering are invariably ill-defined, multivariate,
and exhibit complex nonlinear dynamical behaviour. The increasing demands
for better process efficiency and high product quality have led to the development
and implementation of advanced control strategies in process plants. These
modern control strategies are based on the use of a mathematical model defined for
the process. Traditionally, linear models have been used to approximate the dynamics
of processes whereas most processes are governed by nonlinear mechanisms.
Since linear systems theory is well-established whereas nonlinear systems theory is
not, recent developments in nonlinear dynamical systems theory present opportunities
for improved approaches in modelling these process systems. It is now known
that a nonlinear description of a process can be obtained from using time-delayed
copies reconstructed from measurements taken from the process. Due to low signal
to noise ratios associated with measured data it is logical to exploit redundant information
in multivariate time signals taken from the systems in reconstructing the
underlying dynamics.
This study investigated the extension of univariate nonlinear time series analysis
to the situation where multivariate measurements are available. Using simulated
data from a coupled continuously stirred tank reactor and measured data from a
flotation process system, the comparative advantages of using multivariate and univariate
state space reconstructions were investigated. With respect to detection of
nonlinearity multivariate surrogate analysis were found to give potentially robust
results because of preservation of cross-correlations among components in the surrogate
data. Multivariate local linear models showed a deterministic structure in both
small and large neighbourhood sizes whereas for scalar embeddings determinism was
defined only in smaller neighbourhood sizes. Non-uniform multivariate embeddings
gave local linear models that resembled models from a trivial reconstruction of the original state space variables. With regard to global nonlinear modelling, multivariate
embeddings gave models with better predictability irrespective of the model
class used. Further improvements in the performance of models were obtained for
multivariate non-uniform embeddings.
A relatively new statistical learning algorithm, the least-squares support vector
machine (LSSVM), was evaluated using multilayer perceptrons (MLP) as a benchmark
in modelling nonlinear time series using simulated and plant data. It was
observed that in the absence of autocorrelations in the variables and sparse data
LSSVMs performed better than MLPs. Simulation of trained models gave consistent
results for the LSSVMs, which was not the case for MLPs. However, the
computational costs incurred in training the LSSVM model was significantly higher
than for MLPs. LSSVMs were found to be insensitive to dimensionality reduction
methods whereas the performance of MLPs degraded with increasing complexity of
the dimension reduction method. No relative merits were found for using complex
subspace dimension reduction methods for the data used. No general conclusions
could be drawn with respect to the relative superiority of one class of models method
over the other.
Spatiotemporal structures are routinely observed in many chemical systems,
such as reactive-diffusion and other pattern forming systems. We investigated the
modelling of spatiotemporal time series using the coupled logistic map lattice as
a case study. It was found that including both spatial and temporal information
improved the performance of the fitted models. However, the superiority of spatiotemporal
embeddings over individual time series was found to be defined for
certain choices of the spatial and temporal embedding parameters.<br>AFRIKAANSE OPSOMMING: Fisiese stelsels wat in prosesingenieurswese voorkom is dikwels nie goed gedefinieer
nie, multiveranderlik en vertoon komplekse nie-lineˆere gedrag. Toenemende vereistes
vir ho¨e prosesdoeltreffendheid en produkgehalte het gelei tot die ontwikkeling en implementering
van gevorderde beheerstrategie¨e vir prosesaanlegte. Hierdie morderne
beheerstrategie¨e is gebaseer op die gebruik van wiskundige prosesmodelle. Lineˆere
modelle word gewoonlik ontwikkel, al is die onderliggende prosesmeganismes in die
algemeen nie-lineˆere, aangesien lineˆere stetselteorie goed gevestig is, en nie-line¨ere
stelselteorie nie. Onlangse verwikkelinge in die teorie van nie-lineˆeredinamiese
stelsels bied egter geleenthede vir verbeterde modellering van prosesstelsels. Dit
is bekend dat ‘n nie-lineˆere beskrywing van ‘n progses verkry kan word deur tydvertraagde
kopie¨e van metings van die prosesse te rekonstrueer. Met die lae seintot-
geraasverhoudings wat met gemete data geassosieer word, is dit logies om die
oortollige informasie in meerveranderlike seine te benut tydens die rekonstruksie
van die onderliggende prosesdinamika.
In die tesis is die uitbreiding van enkel-veranderlike nie-lineˆere tydreeksontleding
na meer-veranderlike stelsels ondersoek. Met data van twee aaneengeskakelde
gesimuleerde geroerde tenkreaktore en werklike data van ‘n flottasieproses, is die
meriete van enkel- en meerveranderlike rekonstruksies van toestandruimtes ondersoek.
Meerveranderlike surrogaatdata-ontleding het nie-lineariteite in die data op
‘n meer robuuste wyse ge¨ıdentifiseer, a.g.v. die behoud van kruis-korrelasies in die
komponente van die data. Meerveranderlike lokale lineˆere modelle het ‘n deterministiese
struktuur in beide klein en groot naasliggende omgewings ge¨ıdentifiseer, terwyl
enkelveranderlike metodes dit slegs vir klein naasliggende omgewings kon doen.
Nie-uniforme meerveranderlike inbeddings het lokale lineˆere modelle gegenereer wat
soos globale modelle afkomstig van triviale rekonstruksies van die data gelyk het.
M.b.t globale nie-lineˆere modellering, het meerveranderlike inbedding deurgaans beter modelle opgelewer. Verdere verbetering in die prestasie van modelle kon
verkry word d.m.v. meerveranderlike nie-uniforme inbedding.
‘n Relatief nuwe statistiese algoritme, die kleinste-kwadrate-steunvektormasjien
(KKSVM) is ge¨evalueer teenoor multilaag-perseptrons (MLP) as ‘n standaard vir
die modellering van nie-lineˆere tydreekse, deur gebruik te maak van gesimuleerde en
werklike aanlegdata. Daar is gevind dat die KKSVM beter presteer het as die MLPs
wanneer die opeenvolgende waarnemings swak gekorreleer en min was relatief tot
die aantal veranderlikes. Die KKSVMs het beduidend langer geneem as die MLPs
om te ontwikkel. Hulle was ook minder sensitief vir die metodes wat gevolg is om
die dimensionaliteit van die data te verlaag, anders as die MLPs. Ook is gevind dat
meer komplekse metodes tot die verlaging van die dimensionaliteit weinig nut gehad
het. Geen algemene gevolgtrekkings kan egter gemaak word m.b.t die verskillende
modelle nie.
Ruimtelik-temporale strukture word algemeen waargeneem in baie chemiese
stelsels, soos reaktiewe diffusie e.a. patroonvormende sisteme. Die modellering van
ruimtelik-temporale stelsels is bestudeer aan die hand van ‘n gekoppelde logistiese
projeksierooster. Insluiting van beide die ruimtelike en temporale inligting het tot
beduidend beter modelle gelei, solank as wat di´e inligting op die regte wyse ontsluit
is.
6
Sümbül, Uygar. "Improved time series reconstruction for dynamic magnetic resonance imaging /." May be available electronically:, 2009. http://proquest.umi.com/login?COPT=REJTPTU1MTUmSU5UPTAmVkVSPTI=&clientId=12498.
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Dahlberg, Love. "Dynamic algorithm selection for machine learning on time series." Thesis, Karlstads universitet, Institutionen för matematik och datavetenskap (from 2013), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-72576.
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We present a software that can dynamically determine what machine learning algorithm is best to use in a certain situation given predefined traits. The produced software uses ideal conditions to exemplify how such a solution could function. The software is designed to train a selection algorithm that can predict the behavior of the specified testing algorithms to derive which among them is the best. The software is used to summarize and evaluate a collection of selection algorithm predictions to determine which testing algorithm was the best during that entire period. The goal of this project is to provide a prediction evaluation software solution can lead towards a realistic implementation.
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Lopez, Farias Rodrigo. "Time series forecasting based on classification of dynamic patterns." Thesis, IMT Alti Studi Lucca, 2015. http://e-theses.imtlucca.it/187/1/Farias_phdthesis.pdf.
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This thesis addresses the problem of designing short-term forecasting
models for water demand time series presenting
nonlinear behaviour difficult to be fitted with single linear
models. These behaviours can be identified and classified
to build specialised models for performing local predictions
given an estimated operational regime. Each behavior class is
seen as a forecasting operation mode that activates a forecasting
model. For this purpose we developed a general modular
framework with three different implementations: An implementation
of a Multi-Model predictor that works with Machine
Learning regressors, clustering algorithms, classification,
and function approximations with the objective of producing
accurate forecasts for short horizons. The second and
third implementations are hybrid algorithms that use qualitative
and quantitative information from time series. The
quantitative component contains the aggregated magnitude
of each period of time and the qualitative component contains
the patterns associated with modes. For the qualitative
component we used a low order Seasonal ARIMA model and
for the qualitative component a k-Nearest Neighbours that
predicts the next pattern used to distribute the aggregated
magnitude given by the Seasonal ARIMA. The third implementation
is based on the same architecture, assuming the
existence of an accurate activity calendar with a sequence of
working and rest days, related to the forecast patterns. This
scheme is extended with a nonlinear filter module for the prediction
of pattern mismatches.
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Correia, Maria Inês Costa. "Cluster analysis of financial time series." Master's thesis, Instituto Superior de Economia e Gestão, 2020. http://hdl.handle.net/10400.5/21016.
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Mestrado em Mathematical Finance<br>Esta dissertação aplica o método da Signature como medida de similaridade entre dois objetos de séries temporais usando as propriedades de ordem 2 da Signature e aplicando-as a um método de Clustering Asimétrico. O método é comparado com uma abordagem de Clustering mais tradicional, onde a similaridade é medida usando Dynamic Time Warping, desenvolvido para trabalhar com séries temporais. O intuito é considerar a abordagem tradicional como benchmark e compará-la ao método da Signature através do tempo de computação, desempenho e algumas aplicações. Estes métodos são aplicados num conjunto de dados de séries temporais financeiras de Fundos Mútuos do Luxemburgo.
Após a revisão da literatura, apresentamos o método Dynamic Time Warping e o método da Signature. Prossegue-se com a explicação das abordagens de Clustering Tradicional, nomeadamente k-Means, e Clustering Espectral Assimétrico, nomeadamente k-Axes, desenvolvido por Atev (2011). O último capítulo é dedicado à Investigação Prática onde os métodos anteriores são aplicados ao conjunto de dados. Os resultados confirmam que o método da Signature têm efectivamente potencial para Machine Learning e previsão, como sugerido por Levin, Lyons and Ni (2013).<br>This thesis applies the Signature method as a measurement of similarities between two time-series objects, using the Signature properties of order 2, and its application to Asymmetric Spectral Clustering. The method is compared with a more Traditional Clustering approach where similarities are measured using Dynamic Time Warping, developed to work with time-series data. The intention for this is to consider the traditional approach as a benchmark and compare it to the Signature method through computation times, performance, and applications. These methods are applied to a financial time series data set of Mutual Exchange Funds from Luxembourg.
After the literature review, we introduce the Dynamic Time Warping method and the Signature method. We continue with the explanation of Traditional Clustering approaches, namely k-Means, and Asymmetric Clustering techniques, namely the k-Axes algorithm, developed by Atev (2011). The last chapter is dedicated to Practical Research where the previous methods are applied to the data set. Results confirm that the Signature method has indeed potential for machine learning and prediction, as suggested by Levin, Lyons, and Ni (2013).<br>info:eu-repo/semantics/publishedVersion
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Rodrigues, Antonio Jose Lopes. "Dynamic regression and supervised learning methods in time series modelling and forecasting." Thesis, Lancaster University, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.364365.
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Ahsan, Ramoza. "Time Series Data Analytics." Digital WPI, 2019. https://digitalcommons.wpi.edu/etd-dissertations/529.
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Given the ubiquity of time series data, and the exponential growth of databases, there has recently been an explosion of interest in time series data mining. Finding similar trends and patterns among time series data is critical for many applications ranging from financial planning, weather forecasting, stock analysis to policy making. With time series being high-dimensional objects, detection of similar trends especially at the granularity of subsequences or among time series of different lengths and temporal misalignments incurs prohibitively high computation costs. Finding trends using non-metric correlation measures further compounds the complexity, as traditional pruning techniques cannot be directly applied. My dissertation addresses these challenges while meeting the need to achieve near real-time responsiveness. First, for retrieving exact similarity results using Lp-norm distances, we design a two-layered time series index for subsequence matching. Time series relationships are compactly organized in a directed acyclic graph embedded with similarity vectors capturing subsequence similarities. Powerful pruning strategies leveraging the graph structure greatly reduce the number of time series as well as subsequence comparisons, resulting in a several order of magnitude speed-up. Second, to support a rich diversity of correlation analytics operations, we compress time series into Euclidean-based clusters augmented by a compact overlay graph encoding correlation relationships. Such a framework supports a rich variety of operations including retrieving positive or negative correlations, self correlations and finding groups of correlated sequences. Third, to support flexible similarity specification using computationally expensive warped distance like Dynamic Time Warping we design data reduction strategies leveraging the inexpensive Euclidean distance with subsequent time warped matching on the reduced data. This facilitates the comparison of sequences of different lengths and with flexible alignment still within a few seconds of response time. Comprehensive experimental studies using real-world and synthetic datasets demonstrate the efficiency, effectiveness and quality of the results achieved by our proposed techniques as compared to the state-of-the-art methods.
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Barbosa, Emanuel Pimentel. "Dynamic Bayesian models for vector time series analysis & forecasting." Thesis, University of Warwick, 1989. http://wrap.warwick.ac.uk/34817/.
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This thesis considers the Bayesian analysis of general multivariate DLM's (Dynamic Linear Models) for vector time series forecasting where the observational variance matrices are unknown. This extends considerably some previous work based on conjugate analysis for a special sub—class of vector DLM's where all marginal univariate models follow the same structure. The new methods developed in this thesis, are shown to have a better performance than other competing approaches to vector DLM analysis, as for instance, the one based on the Student t filter. Practical aspects of implementation of the new methods, as well as some theoretical properties are discussed, further model extensions are considered, including non—linear models and some applications with real and simulated data are provided.
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Speakman, John Daniel. "Multivariate, dynamic time series modelling of calls to NHS Direct." Thesis, Lancaster University, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.543983.
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Grziska, Martin. "Multivariate GARCH and dynamic copula models for financial time series." Diss., Ludwig-Maximilians-Universität München, 2014. http://nbn-resolving.de/urn:nbn:de:bvb:19-179219.
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This thesis presents several non-parametric and parametric models for estimating dynamic dependence between financial time series and evaluates their ability to precisely estimate risk
measures. Furthermore, the different dependence models are used to analyze the integration
of emerging markets into the world economy. In order to analyze numerous dependence structures and to discover possible asymmetries, two distinct model classes are investigated: the
multivariate GARCH and Copula models. On the theoretical side a new dynamic dependence
structure for multivariate Archimedean Copulas is introduced which lifts the prevailing restriction to two dimensions and extends the multivariate dynamic Archimedean Copulas to more than two dimensions. On this basis a new mixture copula is presented using the newly invented multivariate dynamic dependence structure for the Archimedean Copulas and mixing it with multivariate elliptical copulas. Simultaneously a new process for modeling the time-varying weights of the mixture copula is introduced: this specification makes it possible to estimate various dependence structures within a single model.
The empirical analysis of different portfolios shows that all equity portfolios and the bond portfolios of the emerging markets exhibit negative asymmetries, i.e. increasing dependence during market downturns. However, the portfolio consisting of the developed market bonds does
not show any negative asymmetries. Overall, the analysis of the risk measures reveals that parametric models display portfolio risk more precisely than non-parametric models. However, no
single parametric model dominates all other models for all portfolios and risk measures. The
investigation of dependence between equity and bond portfolios of developed countries, proprietary, and secondary emerging markets reveals that secondary emerging markets are less
integrated into the world economy than proprietary. Thus, secondary emerging markets are
more suitable to diversify a portfolio consisting of developed equity or bond indices than proprietary
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Lyubchyk, Leonid, Vladislav Kolbasin, and Galina Grinberg. "Nonlinear dynamic system kernel based reconstruction from time series data." Thesis, ТВіМС, 2015. http://repository.kpi.kharkov.ua/handle/KhPI-Press/36826.
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A unified approach to reccurent kernel identification algorithms design is proposed. In order to fix the auxiliary vector dimension, the reduced order model kernel method is proposed and proper reccurent identification algorithms are designed.
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Altiparmak, Fatih. "Online Management and Mining of Heteregenous and Dynamic Time-Series." The Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1204314683.
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Barbosa, Emanuel Pimentel. "Dynamic Baysian models for vector time series analysis and forecasting." Online version, 1989. http://ethos.bl.uk/OrderDetails.do?did=1&uin=uk.bl.ethos.254394.
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Chen, Wilson Ye. "New Advances in Dynamic Risk Models." Thesis, The University of Sydney, 2016. http://hdl.handle.net/2123/16953.
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The central theme of the entire thesis is to explore new ways of modelling the time-varying conditional distributions of financial asset returns. Connected by this central theme, the thesis is separated into three main parts. The first part is on modelling the time-varying variances of financial returns, where the idea of flexibly modelling the news impact curve in a GARCH model is extended to build a more general functional coefficient semiparametric volatility model. It is shown that most existing GARCH models can be written as special cases of the new functional coefficient model. The coefficient function is approximated by a regression spline. An adaptive MCMC algorithm is developed to simulate from the joint posterior of knot configurations and the spline coefficients. The second part is on modelling insurance loss using flexible Tukey family of quantile distributions, such as the g-and-h and g-and-k. The key contribution here is to propose a new estimator for the parameters of the Tukey family of distributions using the idea of L-moments. The estimator is shown to be more statistically efficient and requires less computing time compared to previously proposed methods. This second part serves as an important prerequisite for last part of the thesis; two important concepts introduced in this part are fundamental to the ideas developed in the final part, namely, the g-and-h quantile function and the method of L-moments. In the final part of the thesis, a functional time series model is developed and applied to model the quantile functions of high frequency financial returns; the model can also be viewed as a time-varying model for symbolic data, where each symbolic observation is a quantile function. A key advantage (and contribution) of this symbolic model is that the likelihood function of high frequency returns is allowed to be efficiently constructed via the g-and-h distribution and L-moments. This further allows a forecast model to be built for the entire quantile function, e.g., for the one-minute returns of an asset in one trading day. An efficient adaptive MCMC algorithm is developed for parameter estimation. A novel mixture distribution for modelling positive random variables is also proposed, which is called the Apatosaurus distribution.
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Silva, Diego Furtado. "Large scale similarity-based time series mining." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-07122017-161346/.
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Time series are ubiquitous in the day-by-day of human beings. A diversity of application domains generate data arranged in time, such as medicine, biology, economics, and signal processing. Due to the great interest in time series, a large variety of methods for mining temporal data has been proposed in recent decades. Several of these methods have one characteristic in common: in their cores, there is a (dis)similarity function used to compare the time series. Dynamic Time Warping (DTW) is arguably the most relevant, studied and applied distance measure for time series analysis. The main drawback of DTW is its computational complexity. At the same time, there are a significant number of data mining tasks, such as motif discovery, which requires a quadratic number of distance computations. These tasks are time intensive even for less expensive distance measures, like the Euclidean Distance. This thesis focus on developing fast algorithms that allow large-scale analysis of temporal data, using similarity-based methods for time series data mining. The contributions of this work have implications in several data mining tasks, such as classification, clustering and motif discovery. Specifically, the main contributions of this thesis are the following: (i) an algorithm to speed up the exact DTW calculation and its embedding into the similarity search procedure; (ii) a novel DTW-based spurious prefix and suffix invariant distance; (iii) a music similarity representation with implications on several music mining tasks, and a fast algorithm to compute it, and; (iv) an efficient and anytime method to find motifs and discords under the proposed prefix and suffix invariant DTW.<br>Séries temporais são ubíquas no dia-a-dia do ser humano. Dados organizados no tempo são gerados em uma infinidade de domínios de aplicação, como medicina, biologia, economia e processamento de sinais. Devido ao grande interesse nesse tipo de dados, diversos métodos de mineração de dados temporais foram propostos nas últimas décadas. Muitos desses métodos possuem uma característica em comum: em seu núcleo, há uma função de (dis)similaridade utilizada para comparar as séries. Dynamic Time Warping (DTW) é indiscutivelmente a medida de distância mais relevante na análise de séries temporais. A principal dificuldade em se utilizar a DTW é seu alto custo computacional. Ao mesmo tempo, algumas tarefas de mineração de séries temporais, como descoberta de motifs, requerem um alto número de cálculos de distância. Essas tarefas despendem um grande tempo de execução, mesmo utilizando-se medidas de distância menos custosas, como a distância Euclidiana. Esta tese se concentra no desenvolvimento de algoritmos eficientes que permitem a análise de dados temporais em larga escala, utilizando métodos baseados em similaridade. As contribuições desta tese têm implicações em variadas tarefas de mineração de dados, como classificação, agrupamento e descoberta de padrões frequentes. Especificamente, as principais contribuições desta tese são: (i) um algoritmo para acelerar o cálculo exato da distância DTW e sua incorporação ao processo de busca por similaridade; (ii) um novo algoritmo baseado em DTW para prover invariância a prefixos e sufixos espúrios no cálculo da distância; (iii) uma representação de similaridade musical com implicações em diferentes tarefas de mineração de dados musicais e um algoritmo eficiente para computá-la; (iv) um método eficiente e anytime para encontrar motifs e discords baseado na medida DTW invariante a prefixos e sufixos.
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Karlon, Kathleen Mary. "Determining optimal architecture for dynamic linear models in time series applications /." Electronic version (PDF), 2006. http://dl.uncw.edu/etd/2006/karlonk/kathleenkarlon.pdf.
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Causebrook, Andrew. "Fault ride-through of wind farms using series dynamic braking resistors." Thesis, University of Newcastle Upon Tyne, 2008. http://hdl.handle.net/10443/513.
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Wind power is one of the world's fastest growing industries. The resulting penetration of wind power has led to substantial changes in requirements for large wind farms. Fault Ride-Through (FRT) was an important new requirement for wind farms to remain connected and actively contribute to system stability during a wide range of network faults. The wind industry responded with several approaches to FRT compliance including dynamic Reactive Power Compensation (dRPC) and pitch control. New requirements, combined with the reduced cost and increased efficiency of power electronic converters has led to the increasing dominance of Variable Speed Wind Turbines (VSWTs). Recent research has therefore focused on VSWTs. This Thesis presents a new technology, invented and developed during my PhD project, which provides a rearguard opportunity for Fixed Speed Wind Turbines (FSWTs) to comply with FRT requirementsu sing a series Dynamic Braking Resistor (sDBR). sDBR contributes directly to the balance of active power during a fault by inserting a series resistor into the generation circuit, increasing generator terminal voltage. The aim of the analysis, simulation and experimental work in this Thesis is to demonstrate the potential and scope of sDBR to contribute to FRT compliance of FSWTs. sDBR is shown to be a simple and effective means of displacing expensive dRPC to achieve full compliance with Great Britain's FRT requirements. It is also shown to be capable of contributing to compliance with the more onerous FRT requirements in conjunction with other technologies. Detailed transient simulations of sDBR were confirmed by experimental results using a 7.5kW test-rig. Although the FSWT market is severely weakened, opportunities remain in niche markets for new and existing wind farms. Continued research into high-speed switching, variable resistance and integrated control could further improve basic sDBR performance. Further research into new applications with distribution networks,s mall wind turbines and doubly-fed induction generators could also extend its application in new markets with longer horizons.
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Johnson, Sharon Emma. "Dynamic linear model representations of trend-projecting and seasonal time series." Thesis, Royal Holloway, University of London, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.325032.
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Basu, Deepankar. "Essays on Dynamic Nonlinear Time Series Models and on Gender Inequality." The Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1211331801.
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Molander, Danielsson Karin. "The dynamic detective : special interest and seriality in contemporary detective series /." Uppsala : [Uppsala universitet], 2002. http://catalogue.bnf.fr/ark:/12148/cb39285060p.
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Shakeri, Mohammad Taghi. "Statistical modelling of medical time series data : the dynamic sway magnetometry test." Thesis, University of Newcastle Upon Tyne, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.369783.
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Kim, Yunmi. "Essays on time series models with dynamic coefficients in macroeconomics and finance /." Thesis, Connect to this title online; UW restricted, 2008. http://hdl.handle.net/1773/7379.
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Cross, Patrick Wilson. "System Modeling and Energy Management Strategy Development for Series Hybrid Vehicles." Thesis, Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/24785.
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A series hybrid electric vehicle is a vehicle that is powered by both an engine and a battery pack. An electric motor provides all of the mechanical motive power to the transmission. Engine power is decoupled from the transmission by converting engine power into electricity which powers the electric motor. The mechanical decoupling of the engine from the transmission allows the engine to be run at any operating point (including off) during vehicle operation while the battery back supplies or consumes the remaining power. Therefore, the engine can be operated at its most efficient operating point or in a high-efficiency operating region.
The first objective of this research is to develop a dynamic model of a series hybrid diesel-electric powertrain for implementation in Simulink. The vehicle of interest is a John Deere M-Gator utility vehicle. This model serves primarily to test energy management strategies, but it can also be used for component sizing given known load profiles for a vehicle.
The second objective of this research is to develop and implement multiple energy management strategies of varying complexity from simple thermostat control to an optimal control law derived using dynamic programming. These energy management strategies are then tested and compared over the criteria of overall fuel efficiency, power availability, battery life, and complexity of implementation. Complexity of implementation is a critical metric for control designers and project managers.
The results show that simple point-based control logic can improve upon thermostat control if engine efficiency maps are known. All control method results depend on the load profile being used for a specific application.
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ZANETTI, CHINI EMILIO. "Essays in nonlinear time series analysis." Doctoral thesis, Università degli Studi di Roma "Tor Vergata", 2013. http://hdl.handle.net/2108/203343.
Full textAbstract:
This paper introduces a variant of the smooth transition autoregression (STAR).Theproposedmodelisabletoparametrizetheasymmetryinthetails of the transition equation by using a particular generalization of the logistic function. The null hypothesis of symmetric adjustment toward a new regime is tested by building two different LM-type tests. The first one maintains the original parametrization, while the second one is based on a third-order expanded auxiliary regression. Three diagnostic tests for no error autocorrelation, no additive asymmetry and parameter constancy are also discussed. The empirical size and power of the new symmetry as well as diagnostic tests are investigated by an extensive Monte Carlo experiment. An empirical application of the so generalized STAR (GSTAR) model to four economic time series reveals that the asymmetry in the transition between two regimes is a feature to be considered for economic analysi
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APRAEZ, CESAR DAVID REVELO. "A HYBRID NEURO- EVOLUTIONARY APPROACH FOR DYNAMIC WEIGHTED AGGREGATION OF TIME SERIES FORECASTERS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2016. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=36950@1.
Full textAbstract:
PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>PROGRAMA DE EXCELENCIA ACADEMICA<br>Estudos empíricos na área de séries temporais indicam que combinar
modelos preditivos, originados a partir de diferentes técnicas de modelagem,
levam a previsões consensuais superiores, em termos de acurácia, às previsões
individuais dos modelos envolvidos na combinação. No presente trabalho é
apresentada uma metodologia de combinação convexa de modelos estatísticos de
previsão, cujo sucesso depende da forma como os pesos de combinação de cada
modelo são estimados. Uma Rede Neural Artificial Perceptron Multi-camada
(Multilayer Perceptron - MLP) é utilizada para gerar dinamicamente vetores de
pesos ao longo do horizonte de previsão, sendo estes dependentes da contribuição
individual de cada previsor observada nos dados históricos da série. O ajuste dos
parâmetros da rede MLP é efetuado através de um algoritmo de treinamento
híbrido, que integra técnicas de busca global, baseadas em computação
evolucionária, junto com o algoritmo de busca local backpropagation, de modo a
otimizar de forma simultânea tanto os pesos quanto a arquitetura da rede, visando,
assim, a gerar de forma automática um modelo de ponderação dinâmica de
previsores de alto desempenho. O modelo proposto, batizado de Neural Expert
Weighting - Genetic Algorithm (NEW-GA), foi avaliado em diversos
experimentos comparativos com outros modelos de ponderação de previsores,
assim como também com os modelos individuais envolvidos na combinação,
contemplando 15 séries temporais divididas em dois estudos de casos: séries de
derivados de petróleo e séries da versão reduzida da competição NN3, uma
competição entre metodologias de previsão, com maior ênfase nos modelos
baseados em Redes Neurais. Os resultados demonstraram o potencial do NEWGA
em fornecer modelos acurados de previsão de séries temporais.<br>Empirical studies on time series indicate that the combination of forecasting
models, generated from different modeling techniques, leads to higher
consen+sus forecasts, in terms of accuracy, than the forecasts of individual
models involved in the combination scheme. In this work, we present a
methodology for convex combination of statistical forecasting models, whose
success depends on how the combination weights of each model are estimated.
An Artificial Neural Network Multilayer Perceptron (MLP) is used to generate
dynamically weighting vectors over the forecast horizon, being dependent on the
individual contribution of each forecaster observed over historical data series. The
MLP network parameters are adjusted via a hybrid training algorithm that
integrates global search techniques, based on evolutionary computation, along
with the local search algorithm backpropagation, in order to optimize
simultaneously both weights and network architecture. This approach aims to
automatically generate a dynamic weighted forecast aggregation model with
high performance. The proposed model, called Neural Expert Weighting -
Genetic Algorithm (NEW-GA), was com- pared with other forecaster
combination models, as well as with the individual models involved in the
combination scheme, comprising 15 time series divided into two case studies:
Petroleum Products and the reduced set of NN3 forecasting competition, a
competition between forecasting methodologies, with greater emphasis on
models based on neural networks. The results obtained demonstrated the
potential of NEW-GA in providing accurate models for time series forecasting.
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Siracusa, Michael Richard 1980. "Dynamic dependence analysis : modeling and inference of changing dependence among multiple time-series." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/53303.
Full textAbstract:
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 183-190).<br>In this dissertation we investigate the problem of reasoning over evolving structures which describe the dependence among multiple, possibly vector-valued, time-series. Such problems arise naturally in variety of settings. Consider the problem of object interaction analysis. Given tracks of multiple moving objects one may wish to describe if and how these objects are interacting over time. Alternatively, consider a scenario in which one observes multiple video streams representing participants in a conversation. Given a single audio stream, one may wish to determine with which video stream the audio stream is associated as a means of indicating who is speaking at any point in time. Both of these problems can be cast as inference over dependence structures. In the absence of training data, such reasoning is challenging for several reasons. If one is solely interested in the structure of dependence as described by a graphical model, there is the question of how to account for unknown parameters. Additionally, the set of possible structures is generally super-exponential in the number of time series. Furthermore, if one wishes to reason about structure which varies over time, the number of structural sequences grows exponentially with the length of time being analyzed. We present tractable methods for reasoning in such scenarios. We consider two approaches for reasoning over structure while treating the unknown parameters as nuisance variables. First, we develop a generalized likelihood approach in which point estimates of parameters are used in place of the unknown quantities. We explore this approach in scenarios in which one considers a small enumerated set of specified structures.<br>(cont.) Second, we develop a Bayesian approach and present a conjugate prior on the parameters and structure of a model describing the dependence among time-series. This allows for Bayesian reasoning over structure while integrating over parameters. The modular nature of the prior we define allows one to reason over a super-exponential number of structures in exponential-time in general. Furthermore, by imposing simple local or global structural constraints we show that one can reduce the exponential-time complexity to polynomial-time complexity while still reasoning over a super-exponential number of candidate structures. We cast the problem of reasoning over temporally evolving structures as inference over a latent state sequence which indexes structure over time in a dynamic Bayesian network. This model allows one to utilize standard algorithms such as Expectation Maximization, Viterbi decoding, forward-backward messaging and Gibbs sampling in order to efficiently reasoning over an exponential number of structural sequences. We demonstrate the utility of our methodology on two tasks: audio-visual association and moving object interaction analysis. We achieve state-of-the-art performance on a standard audio-visual dataset and show how our model allows one to tractably make exact probabilistic statements about interactions among multiple moving objects.<br>by Michael Richard Siracusa.<br>Ph.D.
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Montgomery, Brandon. "Evaluating Dynamic Soil Change in the Barnes Soil Series Across Eastern North Dakota." Thesis, North Dakota State University, 2015. https://hdl.handle.net/10365/27384.
Full textAbstract:
Quantifying long-term, global soil change is of the utmost importance as the human population continues growing and food security needs intensify. North Dakota presents a unique opportunity to study dynamic soil change because of its agricultural prominence and extensive soil survey data. A resampling method to characterize soil change from legacy soil survey data was utilized on a benchmark soil series, the Barnes, in North Dakota. Significant decreases (p<0.05) in soil organic carbon (SOC) were measured in surface horizons of three Barnes pedons, and depending upon management practices, morphologic changes ranged from highly eroded, with the complete loss of the A horizon, at two sites, to non-eroded conditions at sites returned to CRP 25 years ago. Additionally, using remotely sensed evapotranspiration (ET) data as a non-biased proxy for soil function shows modeling potential. These results serve as a proof of concept and demonstrate the need for more comprehensive research.<br>United States Department of Agriculture-Natural Resources Conservation Service?s National Soil Survey Center
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Oh, Sang Min. "Switching linear dynamic systems with higher-order temporal structure." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/29698.
Full textAbstract:
Thesis (Ph.D)--Computing, Georgia Institute of Technology, 2010.<br>Committee Chair: Dellaert, Frank; Committee Co-Chair: Rehg, James; Committee Member: Bobick, Aaron; Committee Member: Essa, Irfan; Committee Member: Smyth, Padhraic. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Conradie, Tanja. "Modelling of nonlinear dynamic systems : using surrogate data methods." Thesis, Stellenbosch : Stellenbosch University, 2000. http://hdl.handle.net/10019.1/51834.
Full textAbstract:
Thesis (MSc)--Stellenbosch University, 2000.<br>ENGLISH ABSTRACT: This study examined nonlinear modelling techniques as applied to dynamic systems, paying
specific attention to the Method of Surrogate Data and its possibilities. Within the field of
nonlinear modelling, we examined the following areas of study: attractor reconstruction, general
model building techniques, cost functions, description length, and a specific modelling
methodology. The Method of Surrogate Data was initially applied in a more conventional
application, i.e. testing a time series for nonlinear, dynamic structure. Thereafter, it was used in a
less conventional application; i.e. testing the residual vectors of a nonlinear model for
membership of identically and independently distributed (i.i.d) noise.
The importance of the initial surrogate analysis of a time series (determining whether the apparent
structure of the time series is due to nonlinear, possibly chaotic behaviour) was illustrated. This
study confrrmed that omitting this crucial step could lead to a flawed conclusion.
If evidence of nonlinear structure in the time series was identified, a radial basis model was
constructed, using sophisticated software based on a specific modelling methodology. The model
is an iterative algorithm using minimum description length as the stop criterion. The residual
vectors of the models generated by the algorithm, were tested for membership of the dynamic
class described as i.i.d noise. The results of this surrogate analysis illustrated that, as the model
captures more of the underlying dynamics of the system (description length decreases), the
residual vector resembles Li.d noise. It also verified that the minimum description length
criterion leads to models that capture the underlying dynamics of the time series, with the residual
vector resembling Li.d noise. In the case of the "worst" model (largest description length), the
residual vector could be distinguished from Li.d noise, confirming that it is not the "best" model.
The residual vector of the "best" model (smallest description length), resembled Li.d noise,
confirming that the minimum description length criterion selects a model that captures the
underlying dynamics of the time series.
These applications were illustrated through analysis and modelling of three time series: a time
series generated by the Lorenz equations, a time series generated by electroencephalograhpic
signal (EEG), and a series representing the percentage change in the daily closing price of the
S&P500 index.<br>AFRIKAANSE OPSOMMING: In hierdie studie ondersoek ons nie-lineere modelleringstegnieke soos toegepas op dinamiese
sisteme. Spesifieke aandag word geskenk aan die Metode van Surrogaat Data en die
moontlikhede van hierdie metode. Binne die veld van nie-lineere modellering het ons die
volgende terreine ondersoek: attraktor rekonstruksie, algemene modelleringstegnieke,
kostefunksies, beskrywingslengte, en 'n spesifieke modelleringsalgoritme. Die Metode and
Surrogaat Data is eerstens vir 'n meer algemene toepassing gebruik wat die gekose tydsreeks vir
aanduidings van nie-lineere, dimanise struktuur toets. Tweedens, is dit vir 'n minder algemene
toepassing gebruik wat die residuvektore van 'n nie-lineere model toets vir lidmaatskap van
identiese en onafhanlike verspreide geraas.
Die studie illustreer die noodsaaklikheid van die aanvanklike surrogaat analise van 'n tydsreeks,
wat bepaal of die struktuur van die tydsreeks toegeskryf kan word aan nie-lineere, dalk chaotiese
gedrag. Ons bevesting dat die weglating van hierdie analise tot foutiewelike resultate kan lei.
Indien bewyse van nie-lineere gedrag in die tydsreeks gevind is, is 'n model van radiale
basisfunksies gebou, deur gebruik te maak van gesofistikeerde programmatuur gebaseer op 'n
spesifieke modelleringsmetodologie. Dit is 'n iteratiewe algoritme wat minimum
beskrywingslengte as die termineringsmaatstaf gebruik. Die model se residuvektore is getoets vir
lidmaatskap van die dinamiese klas wat as identiese en onafhanlike verspreide geraas bekend
staan. Die studie verifieer dat die minimum beskrywingslengte as termineringsmaatstaf weI
aanleiding tot modelle wat die onderliggende dinamika van die tydsreeks vasvang, met die
ooreenstemmende residuvektor wat nie onderskei kan word van indentiese en onafhanklike
verspreide geraas nie. In die geval van die "swakste" model (grootse beskrywingslengte), het die
surrogaat analise gefaal omrede die residuvektor van indentiese en onafhanklike verspreide
geraas onderskei kon word. Die residuvektor van die "beste" model (kleinste
beskrywingslengte), kon nie van indentiese en onafhanklike verspreide geraas onderskei word nie
en bevestig ons aanname.
Hierdie toepassings is aan die hand van drie tydsreekse geillustreer: 'n tydsreeks wat deur die
Lorenz vergelykings gegenereer is, 'n tydsreeks wat 'n elektroenkefalogram voorstel en derdens,
'n tydsreeks wat die persentasie verandering van die S&P500 indeks se daaglikse sluitingsprys
voorstel.
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Hudson, Brent. "Modelling the Covariance Dynamics of Multivariate Financial Time Series." Thesis, The University of Sydney, 2011. http://hdl.handle.net/2123/8086.
Full textAbstract:
Investor performance in financial markets can be significantly affected by their ability to model market volatility and correlation over time. The effectiveness of various market activities such as option pricing, portfolio optimisation and risk management rely on the accuracy of such modelling. This thesis proposes a series of multivariate GARCH models that attempt to accurately capture the volatility and correlation dynamics of stock returns. A Bayesian approach is utilised to estimate model parameters, extending classical maximum likelihood (ML) approaches commonly used in the literature for these types of models. A Bayesian prior distribution is proposed for a VECH model that expands the model parameter space and implicitly enforces necessary and sufficient conditions for its positive definiteness and covariance stationarity. An application to a set of US and UK stock indices supports this approach for both parameter and volatility estimation compared to classical ML applied to a competing BEKK model. Volatility asymmetry in stock returns is also discussed, and model selection techniques applied to an extended VECH model to determine the location and size of the asymmetry for international stock markets. In addition to asymmetry, an allowance is made for skewness and excess kurtosis in a proposed copula-GARCH model and is shown to exist in returns for an arbitrary stock portfolio. Moreover, the proposed model also performs well in estimating Value at Risk (VaR) for this portfolio, compared to other univariate and multivariate GARCH models considered. This thesis demonstrates the advantages of using the Bayesian approach for parameter estimation over classical ML, as well as the need to accurately capture the many properties of stock returns in order to improve the modelling of market volatility and correlation.
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Robinson, Jace D. "A Model for Seasonal Dynamic Networks." Wright State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=wright1525195522616039.
Full text
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Birr, Stefan David [Verfasser], Holger [Gutachter] Dette, and Herold [Gutachter] Dehling. "Analyzing dynamic dependencies in time series / Stefan David Birr ; Gutachter: Holger Dette, Herold Dehling." Bochum : Ruhr-Universität Bochum, 2017. http://d-nb.info/1137380101/34.
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Ho, Terrence Tian-Jian. "Dynamic modeling, simulation, and control of a series resonant converter with clamped capacitor voltage." Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/36428.
Full textAbstract:
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.<br>Includes bibliographical references (leaves 173-174).<br>by Terrence Tian-Jian Ho.<br>M.S.
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Wegner, Maus Victor, Gilberto Camara, Marius Appel, and Edzer Pebesma. "dtwSat: Time-Weighted Dynamic Time Warping for Satellite Image Time Series Analysis in R." Foundation for Open Access Statistics, 2019. http://epub.wu.ac.at/6808/1/v88i05.pdf.
Full textAbstract:
The opening of large archives of satellite data such as LANDSAT, MODIS and the SENTINELs has given researchers unprecedented access to data, allowing them to better quantify and understand local and global land change. The need to analyze such large data sets has led to the development of automated and semi-automated methods for satellite image time series analysis. However, few of the proposed methods for remote sensing time series analysis are available as open source software. In this paper we present the R package dtwSat. This package provides an implementation of the time-weighted dynamic time warping method for land cover mapping using sequence of multi-band satellite images. Methods based on dynamic time warping are flexible to handle irregular sampling and out-of-phase time series, and they have achieved significant results in time series analysis. Package dtwSat is available from the Comprehensive R Archive Network (CRAN) and contributes to making methods for satellite time series analysis available to a larger audience. The package supports the full cycle of land cover classification using image time series, ranging from selecting temporal patterns to visualizing and assessing the results.
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Jähnichen, Patrick. "Time Dynamic Topic Models." Doctoral thesis, Universitätsbibliothek Leipzig, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-200796.
Full textAbstract:
Information extraction from large corpora can be a useful tool for many applications in industry and academia. For instance, political communication science has just recently begun to use the opportunities that come with the availability of massive amounts of information available through the Internet and the computational tools that natural language processing can provide. We give a linguistically motivated interpretation of topic modeling, a state-of-the-art algorithm for extracting latent semantic sets of words from large text corpora, and extend this interpretation to cover issues and issue-cycles as theoretical constructs coming from political communication science. We build on a dynamic topic model, a model whose semantic sets of words are allowed to evolve over time governed by a Brownian motion stochastic process and apply a new form of analysis to its result. Generally this analysis is based on the notion of volatility as in the rate of change of stocks or derivatives known from econometrics. We claim that the rate of change of sets of semantically related words can be interpreted as issue-cycles, the word sets as describing the underlying issue. Generalizing over the existing work, we introduce dynamic topic models that are driven by general (Brownian motion is a special case of our model) Gaussian processes, a family of stochastic processes defined by the function that determines their covariance structure. We use the above assumption and apply a certain class of covariance functions to allow for an appropriate rate of change in word sets while preserving the semantic relatedness among words. Applying our findings to a large newspaper data set, the New York Times Annotated corpus (all articles between 1987 and 2007), we are able to identify sub-topics in time, \\\\textit{time-localized topics} and find patterns in their behavior over time. However, we have to drop the assumption of semantic relatedness over all available time for any one topic. Time-localized topics are consistent in themselves but do not necessarily share semantic meaning between each other. They can, however, be interpreted to capture the notion of issues and their behavior that of issue-cycles.
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Wang, Chiying. "Contributions to Collective Dynamical Clustering-Modeling of Discrete Time Series." Digital WPI, 2016. https://digitalcommons.wpi.edu/etd-dissertations/198.
Full textAbstract:
The analysis of sequential data is important in business, science, and engineering, for tasks such as signal processing, user behavior mining, and commercial transactions analysis. In this dissertation, we build upon the Collective Dynamical Modeling and Clustering (CDMC) framework for discrete time series modeling, by making contributions to clustering initialization, dynamical modeling, and scaling.
We first propose a modified Dynamic Time Warping (DTW) approach for clustering initialization within CDMC. The proposed approach provides DTW metrics that penalize deviations of the warping path from the path of constant slope. This reduces over-warping, while retaining the efficiency advantages of global constraint approaches, and without relying on domain dependent constraints.
Second, we investigate the use of semi-Markov chains as dynamical models of temporal sequences in which state changes occur infrequently. Semi-Markov chains allow explicitly specifying the distribution of state visit durations. This makes them superior to traditional Markov chains, which implicitly assume an exponential state duration distribution.
Third, we consider convergence properties of the CDMC framework. We establish convergence by viewing CDMC from an Expectation Maximization (EM) perspective. We investigate the effect on the time to convergence of our efficient DTW-based initialization technique and selected dynamical models. We also explore the convergence implications of various stopping criteria.
Fourth, we consider scaling up CDMC to process big data, using Storm, an open source distributed real-time computation system that supports batch and distributed data processing.
We performed experimental evaluation on human sleep data and on user web navigation data. Our results demonstrate the superiority of the strategies introduced in this dissertation over state-of-the-art techniques in terms of modeling quality and efficiency.
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Knox, Brian T. "Design of a Biped Robot Capable of Dynamic Maneuvers." The Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1228145660.
Full text
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Weiss, Christoph. "Essays in hierarchical time series forecasting and forecast combination." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/274757.
Full textAbstract:
This dissertation comprises of three original contributions to empirical forecasting research. Chapter 1 introduces the dissertation. Chapter 2 contributes to the literature on hierarchical time series (HTS) modelling by proposing a disaggregated forecasting system for both inflation rate and its volatility. Using monthly data that underlies the Retail Prices Index for the UK, we analyse the dynamics of the inflation process. We examine patterns in the time-varying covariation among product-level inflation rates that aggregate up to industry-level inflation rates that in turn aggregate up to the overall inflation rate. The aggregate inflation volatility closely tracks the time path of this covariation, which is seen to be driven primarily by the variances of common shocks shared by all products, and by the covariances between idiosyncratic product-level shocks. We formulate a forecasting system that comprises of models for mean inflation rate and its variance, and exploit the index structure of the aggregate inflation rate using the HTS framework. Using a dynamic model selection approach to forecasting, we obtain forecasts that are between 9 and 155 % more accurate than a SARIMA-GARCH(1,1) for the aggregate inflation volatility. Chapter 3 is on improving forecasts using forecast combinations. The paper documents the software implementation of the open source R package for forecast combination that we coded and published on the official R package depository, CRAN. The GeomComb package is the only R package that covers a wide range of different popular forecast combination methods. We implement techniques from 3 broad categories: (a) simple non-parametric methods, (b) regression-based methods, and (c) geometric (eigenvector) methods, allowing for static or dynamic estimation of each approach. Using S3 classes/methods in R, the package provides a user-friendly environment for applied forecasting, implementing solutions for typical issues related to forecast combination (multicollinearity, missing values, etc.), criterion-based optimisation for several parametric methods, and post-fit functions to rationalise and visualise estimation results. The package has been listed in the official R Task Views for Time Series Analysis and for Official Statistics. The brief empirical application in the paper illustrates the package’s functionality by estimating forecast combination techniques for monthly UK electricity supply. Chapter 4 introduces HTS forecasting and forecast combination to a healthcare staffing context. A slowdown of healthcare budget growth in the UK that does not keep pace with growth of demand for hospital services made efficient cost planning increasingly crucial for hospitals, in particular for staff which accounts for more than half of hospitals’ expenses. This is facilitated by accurate forecasts of patient census and churn. Using a dataset of more than 3 million observations from a large UK hospital, we show how HTS forecasting can improve forecast accuracy by using information at different levels of the hospital hierarchy (aggregate, emergency/electives, divisions, specialties), compared to the naïve benchmark: the seasonal random walk model applied to the aggregate. We show that forecast combination can improve accuracy even more in some cases, and leads to lower forecast error variance (decreasing forecasting risk). We propose a comprehensive parametric approach to use forecasts in a nurse staffing model that has the aim of minimising cost while satisfying that the care requirements (e.g. nurse hours per patient day thresholds) are met.
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Paltrinieri, Federico. "Modeling temporal networks with dynamic stochastic block models." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18805/.
Full textAbstract:
Osservando il recente interesse per le reti dinamiche temporali e l'ampio numero di campi di applicazione, questa tesi ha due principali propositi: primo, di analizzare alcuni modelli teorici di reti temporali, specialmente lo stochastic blockmodel dinamico, al fine di descrivere la dinamica di sistemi reali e fare previsioni. Il secondo proposito della tesi è quello di creare due nuovi modelli teorici, basati sulla teoria dei processi autoregressivi, dai quali inferire nuovi parametri dalle reti temporali, come la matrice di evoluzione di stato e una migliore stima della varianza del rumore del processo di evoluzione temporale. Infine, tutti i modelli sono testati su un data set interbancario: questi rivelano la presenza di un evento atteso che divide la rete temporale in due periodi distinti con differenti configurazioni e parametri.
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Du, Yang. "Comparison of change-point detection algorithms for vector time series." Thesis, Linköpings universitet, Statistik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-59925.
Full textAbstract:
Change-point detection aims to reveal sudden changes in sequences of data. Special attention has been paid to the detection of abrupt level shifts, and applications of such techniques can be found in a great variety of fields, such as monitoring of climate change, examination of gene expressions and quality control in the manufacturing industry. In this work, we compared the performance of two methods representing frequentist and Bayesian approaches, respectively. The frequentist approach involved a preliminary search for level shifts using a tree algorithm followed by a dynamic programming algorithm for optimizing the locations and sizes of the level shifts. The Bayesian approach involved an MCMC (Markov chain Monte Carlo) implementation of a method originally proposed by Barry and Hartigan. The two approaches were implemented in R and extensive simulations were carried out to assess both their computational efficiency and ability to detect abrupt level shifts. Our study showed that the overall performance regarding the estimated location and size of change-points was comparable for the Bayesian and frequentist approach. However, the Bayesian approach performed better when the number of change-points was small; whereas the frequentist became stronger when the change-point proportion increased. The latter method was also better at detecting simultaneous change-points in vector time series. Theoretically, the Bayesian approach has a lower computational complexity than the frequentist approach, but suitable settings for the combined tree and dynamic programming can greatly reduce the processing time.
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Oguz, Gulcin. "Performance Of A Dynamic Voltage Restorer For A Practical Situation." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/12605617/index.pdf.
Full textAbstract:
Among most severe power system disturbances those degrading power quality are voltage sags and transient interruptions. Even voltage sags lasting only a few tens of
milliseconds are enough to bring entire production lines to standstill, causing considerable economic damage as well as endangering the production equipment. Therefore necessary measures have to be taken to protect sensitive loads which are susceptible to these voltage disturbances. Among the solution candidates such as, Uninterruptible Power Supplies, Motor-Generator Sets, etc, Dynamic Voltage Restorer (DVR) which is an effective custom power device has been proposed to mitigate such bus voltage sags on sensitive loads with its excellent dynamic performance.
In this study, load side connected shunt converter topology was chosen for the implementation of DVR. The performance DVR was tried to be improved by improving the control strategy used. Super Film located in Gaziantep which is one of
the SANKO subsidiary company was chosen to simulate the operation of DVR as actual case of Turkish industry. All the simulations in this study were carried on PSCAD/EMTDC Software.
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Phan, Thi-Thu-Hong. "Elastic matching for classification and modelisation of incomplete time series." Thesis, Littoral, 2018. http://www.theses.fr/2018DUNK0483/document.
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Les données manquantes constituent un challenge commun en reconnaissance de forme et traitement de signal. Une grande partie des techniques actuelles de ces domaines ne gère pas l'absence de données et devient inutilisable face à des jeux incomplets. L'absence de données conduit aussi à une perte d'information, des difficultés à interpréter correctement le reste des données présentes et des résultats biaisés notamment avec de larges sous-séquences absentes. Ainsi, ce travail de thèse se focalise sur la complétion de larges séquences manquantes dans les séries monovariées puis multivariées peu ou faiblement corrélées. Un premier axe de travail a été une recherche d'une requête similaire à la fenêtre englobant (avant/après) le trou. Cette approche est basée sur une comparaison de signaux à partir d'un algorithme d'extraction de caractéristiques géométriques (formes) et d'une mesure d'appariement élastique (DTW - Dynamic Time Warping). Un package R CRAN a été développé, DTWBI pour la complétion de série monovariée et DTWUMI pour des séries multidimensionnelles dont les signaux sont non ou faiblement corrélés. Ces deux approches ont été comparées aux approches classiques et récentes de la littérature et ont montré leur faculté de respecter la forme et la dynamique du signal. Concernant les signaux peu ou pas corrélés, un package DTWUMI a aussi été développé. Le second axe a été de construire une similarité floue capable de prender en compte les incertitudes de formes et d'amplitude du signal. Le système FSMUMI proposé est basé sur une combinaison floue de similarités classiques et un ensemble de règles floues. Ces approches ont été appliquées à des données marines et météorologiques dans plusieurs contextes : classification supervisée de cytogrammes phytoplanctoniques, segmentation non supervisée en états environnementaux d'un jeu de 19 capteurs issus d'une station marine MAREL CARNOT en France et la prédiction météorologique de données collectées au Vietnam<br>Missing data are a prevalent problem in many domains of pattern recognition and signal processing. Most of the existing techniques in the literature suffer from one major drawback, which is their inability to process incomplete datasets. Missing data produce a loss of information and thus yield inaccurate data interpretation, biased results or unreliable analysis, especially for large missing sub-sequence(s). So, this thesis focuses on dealing with large consecutive missing values in univariate and low/un-correlated multivariate time series. We begin by investigating an imputation method to overcome these issues in univariate time series. This approach is based on the combination of shape-feature extraction algorithm and Dynamic Time Warping method. A new R-package, namely DTWBI, is then developed. In the following work, the DTWBI approach is extended to complete large successive missing data in low/un-correlated multivariate time series (called DTWUMI) and a DTWUMI R-package is also established. The key of these two proposed methods is that using the elastic matching to retrieving similar values in the series before and/or after the missing values. This optimizes as much as possible the dynamics and shape of knowledge data, and while applying the shape-feature extraction algorithm allows to reduce the computing time. Successively, we introduce a new method for filling large successive missing values in low/un-correlated multivariate time series, namely FSMUMI, which enables to manage a high level of uncertainty. In this way, we propose to use a novel fuzzy grades of basic similarity measures and fuzzy logic rules. Finally, we employ the DTWBI to (i) complete the MAREL Carnot dataset and then we perform a detection of rare/extreme events in this database (ii) forecast various meteorological univariate time series collected in Vietnam
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鍾鴻源. "Applications of series approaches in dynamic systems." Thesis, 1987. http://ndltd.ncl.edu.tw/handle/16344415980733695056.
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Hwang, SangPil. "Dynamic time series analysis using logistic function." 2004. http://www.lib.ncsu.edu/theses/available/etd-04272004-161352/unrestricted/etd.pdf.
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Yan, Ting-chia, and 楊定家. "Dynamic Sensitivity Analysis of Series-Parallel Machine Tool." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/01334057800598217353.
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碩士<br>國立中正大學<br>機械工程所<br>94<br>A hybrid machine tool developed by the Industry Technology Research Institute (ITRI) in Taiwan is used to study the dynamic sensitivity of the 3-PRS series-parallel mechanism. In recent years, the development of machine tools trends toward high accuracy. The accuracy of machine tools is affected greatly by structure vibration. In order to improve system characteristics, it is necessary to study the dynamic sensitivity of the mechanism.
The design variables of the mechanism considered in this study include the radii of the base and moving platforms; lengths, densities and radii of the links; the stiffness of revolute and spherical joints. These parameters will greatly affect the dynamic characteristics of the mechanism. The Newtonian approach is employed to formulate the system equations of motion and the Runge-Kutta method is applied to simulate the dynamic responses of tool tip with the design variables. The relation between sliders’ locations and the tool tip profile is derived by the inverse kinematics. A linearization technique is applied to deal with the complex trigonometric differential equations. The dynamic sensitivity can be analyzed by solving the linearized system equations of motion, and the sensitivity of design parameters and different sliders’ location on the natural frequencies are discussed.
Based on the numerical results, it can be found that the natural frequencies associated with the rotational displacements of the moving platform are affected by the radius of the moving platform. The natural frequencies associated with the degree of freedom of links are affected by the parameters of links or the stiffness of revolute joints. The highest natural frequency is only affected by the radius of the moving platform and stiffness of spherical joints. Besides, some system natural frequencies are not sensitive to sliders’ locations when the corresponding mode shapes in the rotational displacements of the moving platform are close to zero.
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Lee, Chi-Ying, and 李奇穎. "Dynamic Competing Model of Non-linear Time Series." Thesis, 1996. http://ndltd.ncl.edu.tw/handle/57884686999651242383.
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碩士<br>國立政治大學<br>應用數學系<br>84<br>In time series analysis, we find often the trend of dynamic
data changingwith time. Using the traditional model fitting
can''t get a good explanationfor dynamic data. Therefore, many
savants developed a lot of methods formodel construction.
However, these methods are usually influenced by personal
viewpoint and experience in model base selection. In this
thesis, we discussedtime-variant time series analysis. First,
we builded a model base to judge inial models by knowledge base.
Then, we set up the genetic relations of themodels'' parameter.
This method is called Time Variant Genetic Algorithm. We use
the data if the number of junior high school mathematic teachers
in Taiwan to ccompare the predictive performance of Time Variant
Genetic Algorithmwith State Space and ARIMA. The forecasting
performance shows the Time VariantGenetic Algorithm takes a
better prediction result.