Global ETD Search
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Showing 1 to 20 of 113 for “"time series models"”.
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Time Series Models for Analyzing Financial Data
… provide a benchmark to evaluate other available models in empirical studies. The classical feature of this class of models is that the conditional variance (second moment) is considered to be time varying. ARCH models are generally estimated assuming conditional error distribution as normal. …
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Bayesian time series models and scalable inference
With large and growing datasets and complex models, there is an increasing need for scalable Bayesian inference. We describe two lines of work to address this need. In the first part, we develop new algorithms for inference in hierarchical Bayesian time series models based on the hidden Markov …
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Unsupervised Latent Debiasing of Time-Series Models
Traditional training regimens for time-series models have been shown to encode the biases from their training corpora into the models themselves. We aim to train unbiased time-series models using existing biased datasets. However, most debiasing techniques rely on explicit labels that encapsulate …
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Copula-Based Zero-Inflated Count Time Series Models
<p>Count time series data are observed in several applied disciplines such as in environmental science, biostatistics, economics, public health, and finance. In some cases, a specific count, say zero, may occur more often than usual. Additionally, serial dependence might be found among these counts …
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Time Series Models for Finance and the Environment
… features of financial and environmental time series. The methodology used in the chapters is based on the novel observation-driven dynamic conditional score (DCS) class of time series models. The first chapter sets up a DCS model based on the Generalised Beta of the second kind …
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Portmanteau Tests For Univariate And Multivariate Time Series Models
… how the number of available observations of a time series can influence its apparent stationarity as measured by two standard tests, namely the standard Dickey-Fuller (DF) test and the Augmented Dickey-Fuller (ADF) test. The univariate time series case is examined. A stationary time series …
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Understanding and Modeling Taxi Demand Using Time Series Models
… and also help drivers to reduce their vacant time.</p> <p>This dissertation focuses on important factors affecting the demand. In the beginning, the impact of price changes on the demand is studied. Chapter One discusses how the seasonal effects and trends are removed from the demand, and then …
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Bayesian multivariate time series models for forecasting European macroeconomic series
… alternative to subjectively adjusted statistical models [see, for example, Phillips (1995a), Todd (1984) and West & Harrison (1989)]. It provides effective standards of forecasting performance and has demonstrated success in forecasting macroeconomic variables. Therefore, there would seem a case …
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Hidden states, hidden structures: Bayesian learning in time series models
… of model structure for a number of hidden-state time series models, within a Bayesian probabilistic framework. Motivating examples are taken from application areas including finance, physical object tracking and audio restoration. The work in this thesis can be broadly divided into three themes: …
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Using Time Series Models for Defect Prediction in Software Release Planning
… a high-quality software release, sufficient time should be allowed for testing and fixing defects. Otherwise, there is a risk of slip in the development schedule and/or software quality. A time series model is used to predict the number of bugs created during development. The model depends on …
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Vector Generalized Linear Time Series Models with an Implementation in R
… of the ARMA-class in the early 1970s many time series (TS) modelling extensions have been proposed involving linear and non-linear structures as part of a huge literature, for instance, the vector-ARMA class for multivariate TS and the ARCH-GARCH-type models for heteroskedasticity. The …
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Maximum likelihood parameter estimation in time series models using sequential Monte Carlo
Time series models are used to characterise uncertainty in many real-world dynamical phenomena. A time series model typically contains a static variable, called parameter, which parametrizes the joint law of the random variables involved in the definition of the model. When a time series model is …
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Three essays on bias, bias reduction and estimation in autoregressive time series models
… of three essays on the subject of autoregressive time series of order one. The first essay derives an approximate bias of the ordinary least squares estimator (OLS) of the autoregressive parameter for series with moderate deviations from a unit root and for a fixed autoregressive coefficient. The …
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Essays on Conditional Heteroscedastic Time Series Models with Asymmetry, Long memory, and Structural Changes
<p>"The volatility of asset returns is usually time-varying, necessitating the introduction of models with a conditional heteroskedastic variance structure. In this dissertation, several existing formulations, motivated by the Generalized Autoregressive Conditional Heteroskedastic (GARCH) type …
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Essays On Robust Estimators For Non-Identically Distributed Observations In Spatial Econometric And Time Series Models
… methods and applications of spatial econometric models and one essay on the generalized autoregressive conditionally heteroskedastic (GARCH)-type models in financial time series. The first essay discusses the heteroskedasticity robust generalized method of moments estimator (RGMME) for the …
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A comparison of advanced time series models for environmental dependent stock recruitment of the western rock lobster
Time series models have been applied in many areas including economics, stuck recruitment and the environment. Most environmental time series involve highly correlated dependent variables, which makes it difficult to apply conventional regression analysis, Traditionally, regression analysis has …
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