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Academic literature on the topic 'Bayesian Structural Time Series Models'

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Published: 10 March 2023

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Journal articles on the topic "Bayesian Structural Time Series Models"

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Almarashi, Abdullah M., and Khushnoor Khan. "Bayesian Structural Time Series." Nanoscience and Nanotechnology Letters 12, no. 1 (January 1, 2020): 54–61. http://dx.doi.org/10.1166/nnl.2020.3083.

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The current study focused on modeling times series using Bayesian Structural Time Series technique (BSTS) on a univariate data-set. Real-life secondary data from stock prices for flying cement covering a period of one year was used for analysis. Statistical results were based on simulation procedures using Kalman filter and Monte Carlo Markov Chain (MCMC). Though the current study involved stock prices data, the same approach can be applied to complex engineering process involving lead times. Results from the current study were compared with classical Autoregressive Integrated Moving Average (ARIMA) technique. For working out the Bayesian posterior sampling distributions BSTS package run with R software was used. Four BSTS models were used on a real data set to demonstrate the working of BSTS technique. The predictive accuracy for competing models was assessed using Forecasts plots and Mean Absolute Percent Error (MAPE). An easyto-follow approach was adopted so that both academicians and practitioners can easily replicate the mechanism. Findings from the study revealed that, for short-term forecasting, both ARIMA and BSTS are equally good but for long term forecasting, BSTS with local level is the most plausible option.
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Hall, Jamie, Michael K. Pitt, and Robert Kohn. "Bayesian inference for nonlinear structural time series models." Journal of Econometrics 179, no. 2 (April 2014): 99–111. http://dx.doi.org/10.1016/j.jeconom.2013.10.016.

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Wang, Yi-Fu, and Tsai-Hung Fan. "A Bayesian analysis on time series structural equation models." Journal of Statistical Planning and Inference 141, no. 6 (June 2011): 2071–78. http://dx.doi.org/10.1016/j.jspi.2010.12.017.

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Brodersen, Kay H., Fabian Gallusser, Jim Koehler, Nicolas Remy, and Steven L. Scott. "Inferring causal impact using Bayesian structural time-series models." Annals of Applied Statistics 9, no. 1 (March 2015): 247–74. http://dx.doi.org/10.1214/14-aoas788.

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Kalinina, Irina A., and Aleksandr P. Gozhyj. "Modeling and forecasting of nonlinear nonstationary processes based on the Bayesian structural time series." Applied Aspects of Information Technology 5, no. 3 (October 25, 2022): 240–55. http://dx.doi.org/10.15276/aait.05.2022.17.

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The article describes an approach to modelling and forecasting non-linear non-stationary time series for various purposes using Bayesian structural time series. The concepts of non-linearity and non-stationarity, as well as methods for processing non-linearity’sand non-stationarity in the construction of forecasting models are considered. The features of the Bayesian approach in the processing of nonlinearities and nonstationaryare presented. An approach to the construction of probabilistic-statistical models based on Bayesian structural models of time series has been studied. Parametric and non-parametric methods for forecasting non-linear and non-stationary time series are considered. Parametric methods include methods: classical autoregressive models, neural networks, models of support vector machines, hidden Markov models. Non-parametric methods include methods: state-space models, functional decomposition models, Bayesian non-parametric models. One of the types of non-parametric models isBayesian structural time series. The main features of constructing structural time series are considered. Models of structural time series are presented. The process of learning the Bayesianstructural model of time series is described. Training is performed in four stages: setting the structure of the model and a priori probabilities; applying a Kalman filter to update state estimates based on observed data;application of the “spike-and-slab”method to select variables in a structural model; Bayesian averaging to combine the results to make a prediction. An algorithm for constructing a Bayesian structural time seriesmodel is presented. Various components of the BSTS model are considered andanalysed, with the help of which the structures of alternative predictive models are formed. As an example of the application of Bayesian structural time series, the problem of predicting Amazon stock prices is considered. The base dataset is amzn_share. After loading, the structure and data types were analysed, and missing values were processed. The data are characterized by irregular registration of observations, which leads to a large number of missing values and “masking” possible seasonal fluctuations. This makes the task of forecasting rather difficult. To restore gaps in the amzn_sharetime series, the linear interpolation method was used. Using a set of statistical tests (ADF, KPSS, PP), the series was tested for stationarity. The data set is divided into two parts: training and testing. The fitting of structural models of time series was performed using the Kalman filterand the Monte Carlo method according to the Markov chain scheme. To estimate and simultaneously regularize the regression coefficients, the spike-and-slab method was applied. The quality of predictive models was assessed.
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AL-Moders, Ali Hussein, and Tasnim H. Kadhim. "Bayesian Structural Time Series for Forecasting Oil Prices." Ibn AL- Haitham Journal For Pure and Applied Sciences 34, no. 2 (April 20, 2021): 100–107. http://dx.doi.org/10.30526/34.2.2631.

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There are many methods of forecasting, and these methods take data only, analyze it, make a prediction by analyzing, neglect the prior information side and do not considering the fluctuations that occur overtime. The best way to forecast oil prices that takes the fluctuations that occur overtime and is updated by entering prior information is the Bayesian structural time series (BSTS) method. Oil prices fluctuations have an important role in economic so predictions of future oil prices that are crucial for many countries whose economies depend mainly on oil, such as Iraq. Oil prices directly affect the health of the economy. Thus, it is necessary to forecast future oil price with models adapted for emerging events. In this article, we study the Bayesian structural time series (BSTS) for forecasting oil prices. Results show that the price of oil will increase to 156.2$ by 2035.
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Chaturvedi, Anoop, and Jitendra Kumar. "Bayesian Unit Root Test for Time Series Models with Structural Breaks." American Journal of Mathematical and Management Sciences 27, no. 1-2 (January 2007): 243–68. http://dx.doi.org/10.1080/01966324.2007.10737699.

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Fildes, Robert. "Forecasting, Structural Time Series Models and the Kalman Filter: Bayesian Forecasting and Dynamic Models." Journal of the Operational Research Society 42, no. 11 (November 1991): 1031–33. http://dx.doi.org/10.1057/jors.1991.194.

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Kang, Jin-Su, Stephen Thomas Downing, Nabangshu Sinha, and Yi-Chieh Chen. "Advancing Causal Inference: Differences-in-Differences vs. Bayesian Structural Time Series Models." Academy of Management Proceedings 2021, no. 1 (August 2021): 15410. http://dx.doi.org/10.5465/ambpp.2021.15410abstract.

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Jeong, Chulwoo, and Jaehee Kim. "Bayesian multiple structural change-points estimation in time series models with genetic algorithm." Journal of the Korean Statistical Society 42, no. 4 (December 2013): 459–68. http://dx.doi.org/10.1016/j.jkss.2013.02.001.

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Dissertations / Theses on the topic "Bayesian Structural Time Series Models"

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Murphy, James Kevin. "Hidden states, hidden structures : Bayesian learning in time series models." Thesis, University of Cambridge, 2014. https://www.repository.cam.ac.uk/handle/1810/250355.

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This thesis presents methods for the inference of system state and the learning 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: system and parameter estimation in linear jump-diffusion systems, non-parametric model (system) estimation and batch audio restoration. For linear jump-diffusion systems, efficient state estimation methods based on the variable rate particle filter are presented for the general linear case (chapter 3) and a new method of parameter estimation based on Particle MCMC methods is introduced and tested against an alternative method using reversible-jump MCMC (chapter 4). Non-parametric model estimation is examined in two settings: the estimation of non-parametric environment models in a SLAM-style problem, and the estimation of the network structure and forms of linkage between multiple objects. In the former case, a non-parametric Gaussian process prior model is used to learn a potential field model of the environment in which a target moves. Efficient solution methods based on Rao-Blackwellized particle filters are given (chapter 5). In the latter case, a new way of learning non-linear inter-object relationships in multi-object systems is developed, allowing complicated inter-object dynamics to be learnt and causality between objects to be inferred. Again based on Gaussian process prior assumptions, the method allows the identification of a wide range of relationships between objects with minimal assumptions and admits efficient solution, albeit in batch form at present (chapter 6). Finally, the thesis presents some new results in the restoration of audio signals, in particular the removal of impulse noise (pops and clicks) from audio recordings (chapter 7).
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Wigren, Richard, and Filip Cornell. "Marketing Mix Modelling: A comparative study of statistical models." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-160082.

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Deciding the optimal media advertisement spending is a complex issue that many companies today are facing. With the rise of new ways to market products, the choices can appear infinite. One methodical way to do this is to use Marketing Mix Modelling (MMM), in which statistical modelling is used to attribute sales to media spendings. However, many problems arise during the modelling. Modelling and mitigation of uncertainty, time-dependencies of sales, incorporation of expert information and interpretation of models are all issues that need to be addressed. This thesis aims to investigate the effectiveness of eight different statistical and machine learning methods in terms of prediction accuracy and certainty, each one addressing one of the previously mentioned issues. It is concluded that while Shapley Value Regression has the highest certainty in terms of coefficient estimation, it sacrifices some prediction accuracy. The overall highest performing model is the Bayesian hierarchical model, achieving both high prediction accuracy and high certainty.
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Rahier, Thibaud. "Réseaux Bayésiens pour fusion de données statiques et temporelles." Thesis, Université Grenoble Alpes (ComUE), 2018. http://www.theses.fr/2018GREAM083/document.

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La prédiction et l'inférence sur des données temporelles sont très souvent effectuées en utilisant uniquement les séries temporelles. Nous sommes convaincus que ces tâches pourraient tirer parti de l'utilisation des métadonnées contextuelles associées aux séries temporelles, telles que l'emplacement, le type, etc. Réciproquement, les tâches de prédiction et d'inférence sur les métadonnées pourraient bénéficier des informations contenues dans les séries temporelles. Cependant, il n'existe pas de méthode standard pour modéliser conjointement les données de séries temporelles et les métadonnées descriptives. De plus, les métadonnées contiennent fréquemment des informations hautement corrélées ou redondantes et peuvent contenir des erreurs et des valeurs manquantes.Nous examinons d’abord le problème de l’apprentissage de la structure graphique probabiliste inhérente aux métadonnées en tant que réseau Bayésien. Ceci présente deux avantages principaux: (i) une fois structurées en tant que modèle graphique, les métadonnées sont plus faciles à utiliser pour améliorer les tâches sur les données temporelles et (ii) le modèle appris permet des tâches d'inférence sur les métadonnées uniquement, telles que l'imputation de données manquantes. Cependant, l'apprentissage de la structure de réseau Bayésien est un défi mathématique conséquent, impliquant un problème d'optimisation NP-difficile. Pour faire face à ce problème, nous présentons un algorithme d'apprentissage de structure sur mesure, inspiré de nouveaux résultats théoriques, qui exploite les dépendances (quasi)-déterministes généralement présentes dans les métadonnées descriptives. Cet algorithme est testé sur de nombreux jeux de données de référence et sur certains jeux de métadonnées industriels contenant des relations déterministes. Dans les deux cas, il s'est avéré nettement plus rapide que l'état de la l'art, et a même trouvé des structures plus performantes sur des données industrielles. De plus, les réseaux Bayésiens appris sont toujours plus parcimonieux et donc plus lisibles.Nous nous intéressons ensuite à la conception d'un modèle qui inclut à la fois des (méta)données statiques et des données temporelles. En nous inspirant des modèles graphiques probabilistes pour les données temporelles (réseaux Bayésiens dynamiques) et de notre approche pour la modélisation des métadonnées, nous présentons une méthodologie générale pour modéliser conjointement les métadonnées et les données temporelles sous forme de réseaux Bayésiens hybrides statiques-dynamiques. Nous proposons deux algorithmes principaux associés à cette représentation: (i) un algorithme d'apprentissage qui, bien qu'optimisé pour les données industrielles, reste généralisable à toute tâche de fusion de données statiques et dynamiques, et (ii) un algorithme d'inférence permettant les d'effectuer à la fois des requêtes sur des données temporelles ou statiques uniquement, et des requêtes utilisant ces deux types de données.%Nous fournissons ensuite des résultats sur diverses applications inter-domaines telles que les prévisions, le réapprovisionnement en métadonnées à partir de séries chronologiques et l’analyse de dépendance d’alarmes en utilisant les données de certains cas d’utilisation difficiles de Schneider Electric.Enfin, nous approfondissons certaines des notions introduites au cours de la thèse, et notamment la façon de mesurer la performance en généralisation d’un réseau Bayésien par un score inspiré de la procédure de validation croisée provenant de l’apprentissage automatique supervisé. Nous proposons également des extensions diverses aux algorithmes et aux résultats théoriques présentés dans les chapitres précédents, et formulons quelques perspectives de recherche
Prediction and inference on temporal data is very frequently performed using timeseries data alone. We believe that these tasks could benefit from leveraging the contextual metadata associated to timeseries - such as location, type, etc. Conversely, tasks involving prediction and inference on metadata could benefit from information held within timeseries. However, there exists no standard way of jointly modeling both timeseries data and descriptive metadata. Moreover, metadata frequently contains highly correlated or redundant information, and may contain errors and missing values.We first consider the problem of learning the inherent probabilistic graphical structure of metadata as a Bayesian Network. This has two main benefits: (i) once structured as a graphical model, metadata is easier to use in order to improve tasks on temporal data and (ii) the learned model enables inference tasks on metadata alone, such as missing data imputation. However, Bayesian network structure learning is a tremendous mathematical challenge, that involves a NP-Hard optimization problem. We present a tailor-made structure learning algorithm, inspired from novel theoretical results, that exploits (quasi)-determinist dependencies that are typically present in descriptive metadata. This algorithm is tested on numerous benchmark datasets and some industrial metadatasets containing deterministic relationships. In both cases it proved to be significantly faster than state of the art, and even found more performant structures on industrial data. Moreover, learned Bayesian networks are consistently sparser and therefore more readable.We then focus on designing a model that includes both static (meta)data and dynamic data. Taking inspiration from state of the art probabilistic graphical models for temporal data (Dynamic Bayesian Networks) and from our previously described approach for metadata modeling, we present a general methodology to jointly model metadata and temporal data as a hybrid static-dynamic Bayesian network. We propose two main algorithms associated to this representation: (i) a learning algorithm, which while being optimized for industrial data, is still generalizable to any task of static and dynamic data fusion, and (ii) an inference algorithm, enabling both usual tasks on temporal or static data alone, and tasks using the two types of data.%We then provide results on diverse cross-field applications such as forecasting, metadata replenishment from timeseries and alarms dependency analysis using data from some of Schneider Electric’s challenging use-cases.Finally, we discuss some of the notions introduced during the thesis, including ways to measure the generalization performance of a Bayesian network by a score inspired from the cross-validation procedure from supervised machine learning. We also propose various extensions to the algorithms and theoretical results presented in the previous chapters, and formulate some research perspectives
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Bracegirdle, C. I. "Inference in Bayesian time-series models." Thesis, University College London (University of London), 2013. http://discovery.ucl.ac.uk/1383529/.

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Time series-data accompanied with a sequential ordering-occur and evolve all around us. Analysing time series is the problem of trying to discern and describe a pattern in the sequential data that develops in a logical way as the series continues, and the study of sequential data has occurred for a long period across a vast array of fields, including signal processing, bioinformatics, and finance-to name but a few. Classical approaches are based on estimating the parameters of temporal evolution of the process according to an assumed model. In econometrics literature, the field is focussed on parameter estimation of linear (regression) models with a number of extensions. In this thesis, I take a Bayesian probabilistic modelling approach in discrete time, and focus on novel inference schemes. Fundamentally, Bayesian analysis replaces parameter estimates by quantifying uncertainty in the value, and probabilistic inference is used to update the uncertainty based on what is observed in practice. I make three central contributions. First, I discuss a class of latent Markov model which allows a Bayesian approach to internal process resets, and show how inference in such a model can be performed efficiently, before extending the model to a tractable class of switching time series models. Second, I show how inference in linear-Gaussian latent models can be extended to allow a Bayesian approach to variance, and develop a corresponding variance-resetting model, the heteroskedastic linear-dynamical system. Third, I turn my attention to cointegration-a headline topic in finance-and describe a novel estimation scheme implied by Bayesian analysis, which I show to be empirically superior to the classical approach. I offer example applications throughout and conclude with a discussion.
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Johnson, Matthew James Ph D. Massachusetts Institute of Technology. "Bayesian time series models and scalable inference." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/89993.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 197-206).
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 model (HMM), hidden semi-Markov model (HSMM), and their Bayesian nonparametric extensions. The HMM is ubiquitous in Bayesian time series models, and it and its Bayesian nonparametric extension, the hierarchical Dirichlet process hidden Markov model (HDP-HMM), have been applied in many settings. HSMMs and HDP-HSMMs extend these dynamical models to provide state-specific duration modeling, but at the cost of increased computational complexity for inference, limiting their general applicability. A challenge with all such models is scaling inference to large datasets. We address these challenges in several ways. First, we develop classes of duration models for which HSMM message passing complexity scales only linearly in the observation sequence length. Second, we apply the stochastic variational inference (SVI) framework to develop scalable inference for the HMM, HSMM, and their nonparametric extensions. Third, we build on these ideas to define a new Bayesian nonparametric model that can capture dynamics at multiple timescales while still allowing efficient and scalable inference. In the second part of this thesis, we develop a theoretical framework to analyze a special case of a highly parallelizable sampling strategy we refer to as Hogwild Gibbs sampling. Thorough empirical work has shown that Hogwild Gibbs sampling works very well for inference in large latent Dirichlet allocation models (LDA), but there is little theory to understand when it may be effective in general. By studying Hogwild Gibbs applied to sampling from Gaussian distributions we develop analytical results as well as a deeper understanding of its behavior, including its convergence and correctness in some regimes.
by Matthew James Johnson.
Ph. D.
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Qiang, Fu. "Bayesian multivariate time series models for forecasting European macroeconomic series." Thesis, University of Hull, 2000. http://hydra.hull.ac.uk/resources/hull:8068.

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Research on and debate about 'wise use' of explicitly Bayesian forecasting procedures has been widespread and often heated. This situation has come about partly in response to the dissatisfaction with the poor forecasting performance of conventional methods and partly in view of the development of computational capacity and macro-data availability. Experience with Bayesian econometric forecasting schemes is still rather limited, but it seems to be an attractive 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 for seeking some additional insights into the important role of such methods in achieving objectives within the macroeconomics profession. The primary concerns of this study, motivated by the apparent deterioration of mainstream macroeconometric forecasts of the world economy in recent years [Wallis (1989), pp.34-43], are threefold. The first is to formalize a thorough, yet simple, methodological framework for empirical macroeconometric modelling in a Bayesian spirit. The second is to investigate whether improved forecasting accuracy is feasible within a European-based multicountry context. This is conducted with particular emphasis on the construction and implementation of Bayesian vector autoregressive (BVAR) models that incorporate both a priori and cointegration restrictions. The third is to extend the approach and apply it to the joint-modelling of system-wide interactions amongst national economies. The intention is to attempt to generate more accurate answers to a variety of practical questions about the future path towards a united Europe. The use of BVARs has advanced considerably. In particular, the value of joint-modelling with time-varying parameters and much more sophisticated prior distributions has been stressed in the econometric methodology literature. See e.g. Doan et al. (1984). Kadiyala and Karlsson (1993, 1997), Litterman (1986a), and Phillips (1995a, 1995b). Although trade-linked multicountry macroeconomic models may not be able to clarify all the structural and finer economic characteristics of each economy, they do provide a flexible and adaptable framework for analysis of global economic issues. In this thesis, the forecasting record for the main European countries is examined using the 'post mortem' of IMF, DECO and EEC sources. The formulation, estimation and selection of BVAR forecasting models, carried out using Microfit, MicroTSP, PcGive and RATS packages, are reported. Practical applications of BVAR models especially address the issues as to whether combinations of forecasts explicitly outperform the forecasts of a single model, and whether the recent failures of multicountry forecasts can be attributed to an increase in the 'internal volatility' of the world economic environment. See Artis and Holly (1992), and Barrell and Pain (1992, p.3). The research undertaken consolidates existing empirical and theoretical knowledge of BVAR modelling. It provides a unified coverage of economic forecasting applications and develops a common, effective and progressive methodology for the European economies. The empirical results reflect that in simulated 'out-of-sample' forecasting performances, the gains in forecast accuracy from imposing prior and long-run constraints are statistically significant, especially for small estimation sample sizes and long forecast horizons.
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Fernandes, Cristiano Augusto Coelho. "Non-Gaussian structural time series models." Thesis, London School of Economics and Political Science (University of London), 1991. http://etheses.lse.ac.uk/1208/.

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This thesis aims to develop a class of state space models for non-Gaussian time series. Our models are based on distributions of the exponential family, such as the Poisson, the negative-binomial, the binomial and the gamma. In these distributions the mean is allowed to change over time through a mechanism which mimics a random walk. By adopting a closed sampling analysis we are able to derive finite dimensional filters, similar to the Kalman filter. These are then used to construct the likelihood function and to make forecasts of future observations. In fact for all the specifications here considered we have been able to show that the predictions give rise to schemes based on an exponentially weighted moving average (EWMA). The models may be extended to include explanatory variables via the kind of link functions that appear in GLIM models. This enables nonstochastic slope and seasonal components to be included. The Poisson, negative binomial and bivariate Poisson models are illustrated by considering applications to real data. Monte Carlo experiments are also conducted in order to investigate properties of maximum likelihood estimators and power studies of a post sample predictive test developed for the Poisson model.
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Queen, Catriona M. "Bayesian graphical forecasting models for business time series." Thesis, University of Warwick, 1991. http://wrap.warwick.ac.uk/4321/.

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This thesis develops three new classes of Bayesian graphical models to forecast multivariate time series. Although these models were originally motivated by the need for flexible and tractable forecasting models appropriate for modelling competitive business markets, they are of theoretical interest in their own right. Multiregression dynamic models are defined to preserve certain conditional independence structures over time. Although these models are typically very non-Gaussian, it is proved that they are simple to update, amenable to practical implementation and promise more efficient identification of causal structures in a time series than has been possible in the past. Dynamic graphical models are defined for multivariate time series for which there is believed to be symmetry between certain subsets of variables and a causal driving mechanism between these subsets. They are a specific type of graphical chain model (Wermuth & Lauritzen, 1990) which are once again typically non- Gaussian. Dynamic graphical models are a combination of multiregression dynamic models and multivariate regression models (Quintana, 1985,87, Quintana & West, 1987,88) and as such, they inherit the simplicity of both these models. Partial segmentation models extend the work of Dickey et al. (1987) to the study of models with latent conditional independence structures. Conjugate Bayesian anaylses are developed for processes whose probability parameters are hypothesised to be dependent, using the fact that a certain likelihood separates given a matrix of likelihood ratios. It is shown how these processes can be represented by undirected graphs and how these help in its reparameterisation into conjugate form.
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Pope, Kenneth James. "Time series analysis." Thesis, University of Cambridge, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.318445.

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Surapaitoolkorn, Wantanee. "Bayesian inference for volatility models in financial time series." Thesis, Imperial College London, 2006. http://hdl.handle.net/10044/1/1249.

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The aim of the thesis is to study the two principal volatility models used in ¯nancial time series, and to perform inference using a Bayesian approach. The ¯rst model is the Deterministic Time-Varying volatility represented by Autoregressive Conditional Heteroscedastic (ARCH) models. The second model is the Stochastic Time Varying volatility or Stochastic Volatility (SV) model. The thesis concentrates on using Financial Foreign Exchange (FX) data including time series for four Asian countries of Thailand, Singapore, Japan and Hong Kong, and FX data sets from other countries. The time period this particular FX data set covers includes the recent biggest crisis in Asian ¯nancial markets in 1997. The analysis involves exploring high frequency ¯nancial FX data where the sets of data used are the daily and hourly opening FX rates. The key development of the thesis is the implementation of changepoint models to allow for non-stationarity in the volatility process. The changepoint approach has only rarely been implemented for volatility data. In this thesis, the changepoint model for SVtype volatility structures is formulated. The variable dimensional nature of the inference problem, that is, that the number as well as the locations of the volatility changepoints are unknown, is acknowledged and incorporated, as are the potential leptokurtic nature of ¯nancial returns. The Bayesian computational approach used for making inference about the model parameters is Markov Chain Monte Carlo (MCMC). Another contribution of this thesis is the study of reparameterizations of parameters in both ARCH and SV models. The objective is to improve the performance of the MCMC method.
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Books on the topic "Bayesian Structural Time Series Models"

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Barber, David, A. Taylan Cemgil, and Silvia Chiappa, eds. Bayesian Time Series Models. Cambridge: Cambridge University Press, 2009. http://dx.doi.org/10.1017/cbo9780511984679.

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Barber, David. Bayesian time series models. Cambridge: Cambridge University Press, 2011.

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C, Spall James, ed. Bayesian analysis of time series and dynamic models. New York: Dekker, 1988.

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Queen, Catriona M. Bayesian graphical forecasting models for business time series. [s.l.]: typescript, 1991.

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Koop, Gary. Bayesian long-run prediction in time series models. Kraków: Cracow Academy of Economics, 1992.

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Das, Monidipa, and Soumya K. Ghosh. Enhanced Bayesian Network Models for Spatial Time Series Prediction. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-27749-9.

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Barbosa, Emanuel Pimentel. Dynamic Bayesian models for vector time series analysis & forecasting. [s.l.]: typescript, 1989.

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1948-, Palm Franz C., and Zellner Arnold, eds. The structural econometric time series analysis approach. Cambridge: Cambridge University Press, 2004.

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Harvey, A. C. Forecasting, structural time series models and the Kalman filter. Cambridge: Cambridge University Press, 1989.

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Forecasting, structural time series models, and the Kalman filter. Cambridge: Cambridge University Press, 1990.

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Book chapters on the topic "Bayesian Structural Time Series Models"

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Aguilar, Omar. "Latent Structure Analyses of Turbulence Data Using Wavelets and Time Series Decompositions." In Bayesian Inference in Wavelet-Based Models, 381–94. New York, NY: Springer New York, 1999. http://dx.doi.org/10.1007/978-1-4612-0567-8_23.

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Hooten, Mevin B., and Trevor J. Hefley. "Time Series Models." In Bringing Bayesian Models to Life, 143–73. Boca Raton, FL : CRC Press, Taylor & Francis Group, 2019.: CRC Press, 2019. http://dx.doi.org/10.1201/9780429243653-16.

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Abril, Juan Carlos. "Structural Time Series Models." In International Encyclopedia of Statistical Science, 1555–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-04898-2_577.

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Berliner, L. Mark. "Hierarchical Bayesian Time Series Models." In Maximum Entropy and Bayesian Methods, 15–22. Dordrecht: Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-011-5430-7_3.

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Broemeling, Lyle D. "Basic Random Models." In Bayesian Analysis of Time Series, 69–92. Boca Raton : CRC Press, Taylor & Francis Group, 2019.: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780429488443-4.

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Broemeling, Lyle D. "Dynamic Linear Models." In Bayesian Analysis of Time Series, 179–220. Boca Raton : CRC Press, Taylor & Francis Group, 2019.: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780429488443-8.

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Ravishanker, Nalini, Balaji Raman, and Refik Soyer. "Bayesian Analysis." In Dynamic Time Series Models using R-INLA, 1–16. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003134039-1.

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Held, Leonhard, and Daniel Sabanés Bové. "Markov Models for Time Series Analysis." In Likelihood and Bayesian Inference, 315–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-60792-3_10.

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Gómez, Víctor. "Multivariate Structural Models." In Linear Time Series with MATLAB and OCTAVE, 245–62. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20790-8_7.

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West, Mike, and Julia Mortera. "Bayesian Models and Methods for Binary Time Series." In Probability and Bayesian Statistics, 487–95. Boston, MA: Springer US, 1987. http://dx.doi.org/10.1007/978-1-4613-1885-9_50.

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Conference papers on the topic "Bayesian Structural Time Series Models"

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Kalinina, Irina, Peter Bidyuk, and Aleksandr Gozhyj. "Construction of Forecast Models based on Bayesian Structural Time Series." In 2022 IEEE 17th International Conference on Computer Sciences and Information Technologies (CSIT). IEEE, 2022. http://dx.doi.org/10.1109/csit56902.2022.10000484.

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Huq, Armana, and Shahrin Islam. "A Bayesian Structural Time Series Model for Assessing Road Traffic Accidents during COVID-19 Period." In The 6th International Conference on Civil, Structural and Transportation Engineering. Avestia Publishing, 2021. http://dx.doi.org/10.11159/iccste21.166.

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Han, Jiyeon, Kyowoon Lee, Anh Tong, and Jaesik Choi. "Confirmatory Bayesian Online Change Point Detection in the Covariance Structure of Gaussian Processes." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/340.

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In the analysis of sequential data, the detection of abrupt changes is important in predicting future events. In this paper, we propose statistical hypothesis tests for detecting covariance structure changes in locally smooth time series modeled by Gaussian Processes (GPs). We provide theoretically justified thresholds for the tests, and use them to improve Bayesian Online Change Point Detection (BOCPD) by confirming statistically significant changes and non-changes. Our Confirmatory BOCPD (CBOCPD) algorithm finds multiple structural breaks in GPs even when hyperparameters are not tuned precisely. We also provide conditions under which CBOCPD provides the lower prediction error compared to BOCPD. Experimental results on synthetic and real-world datasets show that our proposed algorithm outperforms existing methods for the prediction of nonstationarity in terms of both regression error and log-likelihood.
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Hoernle, Nicholas, Kobi Gal, Barbara Grosz, Leilah Lyons, Ada Ren, and Andee Rubin. "Interpretable Models for Understanding Immersive Simulations." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/321.

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This paper describes methods for comparative evaluation of the interpretability of models of high dimensional time series data inferred by unsupervised machine learning algorithms. The time series data used in this investigation were logs from an immersive simulation like those commonly used in education and healthcare training. The structures learnt by the models provide representations of participants' activities in the simulation which are intended to be meaningful to people's interpretation. To choose the model that induces the best representation, we designed two interpretability tests, each of which evaluates the extent to which a model’s output aligns with people’s expectations or intuitions of what has occurred in the simulation. We compared the performance of the models on these interpretability tests to their performance on statistical information criteria. We show that the models that optimize interpretability quality differ from those that optimize (statistical) information theoretic criteria. Furthermore, we found that a model using a fully Bayesian approach performed well on both the statistical and human-interpretability measures. The Bayesian approach is a good candidate for fully automated model selection, i.e., when direct empirical investigations of interpretability are costly or infeasible.
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HOLLKAMP, J., and S. BATILL. "Time series models for nonlinear systems." In 30th Structures, Structural Dynamics and Materials Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1989. http://dx.doi.org/10.2514/6.1989-1197.

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Shaffery, Peter, Rui Yang, and Yingchen Zhang. "Bayesian Structural Time Series for Behind-the-Meter Photovoltaic Disaggregation." In 2020 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT). IEEE, 2020. http://dx.doi.org/10.1109/isgt45199.2020.9087675.

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REILLY, JACK, and BRANKO GLISIC. "Long Term Sensor Malfunction Detection and Data Regeneration using Autoregressive Time Series Models." In Structural Health Monitoring 2017. Lancaster, PA: DEStech Publications, Inc., 2017. http://dx.doi.org/10.12783/shm2017/14211.

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AMER, AHMAD, and FOTIS KOPSAFTOPOULOS. "Probabilistic Damage Quantification via the Integration of Non- parametric Time-Series and Gaussian Process Regression Models." In Structural Health Monitoring 2019. Lancaster, PA: DEStech Publications, Inc., 2019. http://dx.doi.org/10.12783/shm2019/32379.

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HOLLKAMP, J., and S. BATILL. "An experimental study of noise bias in discrete time series models." In 30th Structures, Structural Dynamics and Materials Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1989. http://dx.doi.org/10.2514/6.1989-1193.

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BATILL, S., and J. HOLLKAMP. "Parameter identification of discrete time series models for transient response prediction." In 29th Structures, Structural Dynamics and Materials Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1988. http://dx.doi.org/10.2514/6.1988-2231.

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Reports on the topic "Bayesian Structural Time Series Models"

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Mazzoni, Silvia, Nicholas Gregor, Linda Al Atik, Yousef Bozorgnia, David Welch, and Gregory Deierlein. Probabilistic Seismic Hazard Analysis and Selecting and Scaling of Ground-Motion Records (PEER-CEA Project). Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, November 2020. http://dx.doi.org/10.55461/zjdn7385.

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This report is one of a series of reports documenting the methods and findings of a multi-year, multi-disciplinary project coordinated by the Pacific Earthquake Engineering Research Center (PEER) and funded by the California Earthquake Authority (CEA). The overall project is titled “Quantifying the Performance of Retrofit of Cripple Walls and Sill Anchorage in Single-Family Wood-Frame Buildings,” henceforth referred to as the “PEER–CEA Project.” The overall objective of the PEER–CEA Project is to provide scientifically based information (e.g., testing, analysis, and resulting loss models) that measure and assess the effectiveness of seismic retrofit to reduce the risk of damage and associated losses (repair costs) of wood-frame houses with cripple wall and sill anchorage deficiencies as well as retrofitted conditions that address those deficiencies. Tasks that support and inform the loss-modeling effort are: (1) collecting and summarizing existing information and results of previous research on the performance of wood-frame houses; (2) identifying construction features to characterize alternative variants of wood-frame houses; (3) characterizing earthquake hazard and ground motions at representative sites in California; (4) developing cyclic loading protocols and conducting laboratory tests of cripple wall panels, wood-frame wall subassemblies, and sill anchorages to measure and document their response (strength and stiffness) under cyclic loading; and (5) the computer modeling, simulations, and the development of loss models as informed by a workshop with claims adjustors. This report is a product of Working Group 3 (WG3), Task 3.1: Selecting and Scaling Ground-motion records. The objective of Task 3.1 is to provide suites of ground motions to be used by other working groups (WGs), especially Working Group 5: Analytical Modeling (WG5) for Simulation Studies. The ground motions used in the numerical simulations are intended to represent seismic hazard at the building site. The seismic hazard is dependent on the location of the site relative to seismic sources, the characteristics of the seismic sources in the region and the local soil conditions at the site. To achieve a proper representation of hazard across the State of California, ten sites were selected, and a site-specific probabilistic seismic hazard analysis (PSHA) was performed at each of these sites for both a soft soil (Vs30 = 270 m/sec) and a stiff soil (Vs30=760 m/sec). The PSHA used the UCERF3 seismic source model, which represents the latest seismic source model adopted by the USGS [2013] and NGA-West2 ground-motion models. The PSHA was carried out for structural periods ranging from 0.01 to 10 sec. At each site and soil class, the results from the PSHA—hazard curves, hazard deaggregation, and uniform-hazard spectra (UHS)—were extracted for a series of ten return periods, prescribed by WG5 and WG6, ranging from 15.5–2500 years. For each case (site, soil class, and return period), the UHS was used as the target spectrum for selection and modification of a suite of ground motions. Additionally, another set of target spectra based on “Conditional Spectra” (CS), which are more realistic than UHS, was developed [Baker and Lee 2018]. The Conditional Spectra are defined by the median (Conditional Mean Spectrum) and a period-dependent variance. A suite of at least 40 record pairs (horizontal) were selected and modified for each return period and target-spectrum type. Thus, for each ground-motion suite, 40 or more record pairs were selected using the deaggregation of the hazard, resulting in more than 200 record pairs per target-spectrum type at each site. The suites contained more than 40 records in case some were rejected by the modelers due to secondary characteristics; however, none were rejected, and the complete set was used. For the case of UHS as the target spectrum, the selected motions were modified (scaled) such that the average of the median spectrum (RotD50) [Boore 2010] of the ground-motion pairs follow the target spectrum closely within the period range of interest to the analysts. In communications with WG5 researchers, for ground-motion (time histories, or time series) selection and modification, a period range between 0.01–2.0 sec was selected for this specific application for the project. The duration metrics and pulse characteristics of the records were also used in the final selection of ground motions. The damping ratio for the PSHA and ground-motion target spectra was set to 5%, which is standard practice in engineering applications. For the cases where the CS was used as the target spectrum, the ground-motion suites were selected and scaled using a modified version of the conditional spectrum ground-motion selection tool (CS-GMS tool) developed by Baker and Lee [2018]. This tool selects and scales a suite of ground motions to meet both the median and the user-defined variability. This variability is defined by the relationship developed by Baker and Jayaram [2008]. The computation of CS requires a structural period for the conditional model. In collaboration with WG5 researchers, a conditioning period of 0.25 sec was selected as a representative of the fundamental mode of vibration of the buildings of interest in this study. Working Group 5 carried out a sensitivity analysis of using other conditioning periods, and the results and discussion of selection of conditioning period are reported in Section 4 of the WG5 PEER report entitled Technical Background Report for Structural Analysis and Performance Assessment. The WG3.1 report presents a summary of the selected sites, the seismic-source characterization model, and the ground-motion characterization model used in the PSHA, followed by selection and modification of suites of ground motions. The Record Sequence Number (RSN) and the associated scale factors are tabulated in the Appendices of this report, and the actual time-series files can be downloaded from the PEER Ground-motion database Portal (https://ngawest2.berkeley.edu/)(link is external).
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