Análisis bayesiano de series temporales PDF Download

Author: Universidad de Las Palmas de Gran Canaria. Seminario de Economía Aplicada
Publisher:
ISBN:
Category :
Languages : es
Pages : 38

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Author: Andy Pole
Publisher: CRC Press
ISBN: 1482267438
Category : Business & Economics
Languages : en
Pages : 432

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Practical in its approach, Applied Bayesian Forecasting and Time Series Analysis provides the theories, methods, and tools necessary for forecasting and the analysis of time series. The authors unify the concepts, model forms, and modeling requirements within the framework of the dynamic linear mode (DLM). They include a complete theoretical development of the DLM and illustrate each step with analysis of time series data. Using real data sets the authors: Explore diverse aspects of time series, including how to identify, structure, explain observed behavior, model structures and behaviors, and interpret analyses to make informed forecasts Illustrate concepts such as component decomposition, fundamental model forms including trends and cycles, and practical modeling requirements for routine change and unusual events Conduct all analyses in the BATS computer programs, furnishing online that program and the more than 50 data sets used in the text The result is a clear presentation of the Bayesian paradigm: quantified subjective judgements derived from selected models applied to time series observations. Accessible to undergraduates, this unique volume also offers complete guidelines valuable to researchers, practitioners, and advanced students in statistics, operations research, and engineering.

Author:
Publisher:
ISBN:
Category :
Languages : es
Pages : 478

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Author: CESAR PEREZ LOPEZ
Publisher: CESAR PEREZ
ISBN:
Category : Mathematics
Languages : es
Pages : 307

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MATLAB cuenta con la herramienta Deep Learning Toolbox que proporciona algoritmos, funciones y aplicaciones para crear, entrenar, visualizar y simular redes neuronales. Puede realizar clasificación, regresión, agrupamiento, reducción de dimensionalidad, pronóstico de series temporales y modelado y control de sistemas dinámicos. Las redes neuronales dinámicas son adecuadas para la predicción de series temporales. Puede utilizar la app Neural Net Time Series para resolver diferentes tipos de problemas de series temporales. Generalmente es mejor comenzar con la GUI y luego usarla para generar automáticamente scripts de línea de comandos. Antes de utilizar cualquiera de los métodos, el primer paso es definir el problema seleccionando un conjunto de datos. Cada GUI tiene acceso a muchos conjuntos de datos de muestra que puede utilizar para experimentar con la caja de herramientas. Si tiene un problema específico que desea resolver, puede cargar sus propios datos en el espacio de trabajo. Con MATLAB es posible resolver tres tipos diferentes de problemas de series temporales. En el primer tipo de problema de series de tiempo, se busca predecir valores futuros de una serie de tiempo y(t) a partir de valores pasados de esa serie de tiempo y valores pasados de una segunda serie de tiempo x(t). Esta forma de predicción se denomina red autorregresiva no lineal con entrada exógena (externa), o NARX. En el segundo tipo de problema de series temporales, sólo hay una serie involucrada. Los valores futuros de una serie temporal y(t) se predicen sólo a partir de valores pasados de esa serie. Esta forma de predicción se llama autorregresiva no lineal o NAR. El tercer problema de series de tiempo es similar al primer tipo, en el sentido de que están involucradas dos series, una serie de entrada (predictores) x(t) y una serie de salida (respuestas) y(t). Este libro desarrolla los métodos de predicción con series temporales a través de redes neuronales con MATLAB.

Author: Mike West
Publisher: Springer Science & Business Media
ISBN: 1475793650
Category : Mathematics
Languages : en
Pages : 720

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In this book we are concerned with Bayesian learning and forecast ing in dynamic environments. We describe the structure and theory of classes of dynamic models, and their uses in Bayesian forecasting. The principles, models and methods of Bayesian forecasting have been developed extensively during the last twenty years. This devel opment has involved thorough investigation of mathematical and sta tistical aspects of forecasting models and related techniques. With this has come experience with application in a variety of areas in commercial and industrial, scientific and socio-economic fields. In deed much of the technical development has been driven by the needs of forecasting practitioners. As a result, there now exists a relatively complete statistical and mathematical framework, although much of this is either not properly documented or not easily accessible. Our primary goals in writing this book have been to present our view of this approach to modelling and forecasting, and to provide a rea sonably complete text for advanced university students and research workers. The text is primarily intended for advanced undergraduate and postgraduate students in statistics and mathematics. In line with this objective we present thorough discussion of mathematical and statistical features of Bayesian analyses of dynamic models, with illustrations, examples and exercises in each Chapter.

Author: Lyle D. Broemeling
Publisher: CRC Press
ISBN: 0429948913
Category : Mathematics
Languages : en
Pages : 211

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In many branches of science relevant observations are taken sequentially over time. Bayesian Analysis of Time Series discusses how to use models that explain the probabilistic characteristics of these time series and then utilizes the Bayesian approach to make inferences about their parameters. This is done by taking the prior information and via Bayes theorem implementing Bayesian inferences of estimation, testing hypotheses, and prediction. The methods are demonstrated using both R and WinBUGS. The R package is primarily used to generate observations from a given time series model, while the WinBUGS packages allows one to perform a posterior analysis that provides a way to determine the characteristic of the posterior distribution of the unknown parameters. Features Presents a comprehensive introduction to the Bayesian analysis of time series. Gives many examples over a wide variety of fields including biology, agriculture, business, economics, sociology, and astronomy. Contains numerous exercises at the end of each chapter many of which use R and WinBUGS. Can be used in graduate courses in statistics and biostatistics, but is also appropriate for researchers, practitioners and consulting statisticians. About the author Lyle D. Broemeling, Ph.D., is Director of Broemeling and Associates Inc., and is a consulting biostatistician. He has been involved with academic health science centers for about 20 years and has taught and been a consultant at the University of Texas Medical Branch in Galveston, The University of Texas MD Anderson Cancer Center and the University of Texas School of Public Health. His main interest is in developing Bayesian methods for use in medical and biological problems and in authoring textbooks in statistics. His previous books for Chapman & Hall/CRC include Bayesian Biostatistics and Diagnostic Medicine, and Bayesian Methods for Agreement.

Author: James Spall
Publisher: CRC Press
ISBN: 9780824779368
Category : Mathematics
Languages : en
Pages : 576

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Contributors present a multidisciplinary overview of the subject covering non-Gaussian models, time and magnitude of changes in dynamic models, image processing based on satellite radiometer measurements, and stationary and nonstationary linear time series models. The topic bridges statistics, econo

Author: Pilar Jara Jiménez
Publisher: Publicacions de la Universitat Jaume I
ISBN: 9788480213882
Category : Mathematics
Languages : es
Pages : 150

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Pràctic manual que introdueix el lector en les sèries temporals i que descriu els conceptes bàsics, a més de fer un estudi exhaustiu de les seues anàlisis amb el procediment ARIMA i els diferents models que aquest procediment inclou. La gran senzillesa expositiva facilita al lector l'anàlisi, l'aplicació i la interpretació dels diferents components de les sèries.

Author: Lyle D. Broemeling
Publisher: CRC Press
ISBN: 9780824775001
Category : Mathematics
Languages : en
Pages : 292

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Author: G. Larry Bretthorst
Publisher: Springer Science & Business Media
ISBN: 146849399X
Category : Mathematics
Languages : en
Pages : 210

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This work is essentially an extensive revision of my Ph.D. dissertation, [1J. It 1S primarily a research document on the application of probability theory to the parameter estimation problem. The people who will be interested in this material are physicists, economists, and engineers who have to deal with data on a daily basis; consequently, we have included a great deal of introductory and tutorial material. Any person with the equivalent of the mathematics background required for the graduate level study of physics should be able to follow the material contained in this book, though not without eIfort. From the time the dissertation was written until now (approximately one year) our understanding of the parameter estimation problem has changed extensively. We have tried to incorporate what we have learned into this book. I am indebted to a number of people who have aided me in preparing this docu ment: Dr. C. Ray Smith, Steve Finney, Juana Sunchez, Matthew Self, and Dr. Pat Gibbons who acted as readers and editors. In addition, I must extend my deepest thanks to Dr. Joseph Ackerman for his support during the time this manuscript was being prepared.