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Linear Time Series with MATLAB and OCTAVE (Statistics and Computing) de Víctor Gómez

Descripción - Reseña del editor This book presents an introduction to linear univariate and multivariate time series analysis, providing brief theoretical insights into each topic, and from the beginning illustrating the theory with software examples. As such, it quickly introduces readers to the peculiarities of each subject from both theoretical and the practical points of view. It also includes numerous examples and real-world applications that demonstrate how to handle different types of time series data. The associated software package, SSMMATLAB, is written in MATLAB and also runs on the free OCTAVE platform. The book focuses on linear time series models using a state space approach, with the Kalman filter and smoother as the main tools for model estimation, prediction and signal extraction. A chapter on state space models describes these tools and provides examples of their use with general state space models. Other topics discussed in the book include ARIMA; and transfer function and structural models; as well as signal extraction using the canonical decomposition in the univariate case, and VAR, VARMA, cointegrated VARMA, VARX, VARMAX, and multivariate structural models in the multivariate case. It also addresses spectral analysis, the use of fixed filters in a model-based approach, and automatic model identification procedures for ARIMA and transfer function models in the presence of outliers, interventions, complex seasonal patterns and other effects like Easter, trading day, etc. This book is intended for both students and researchers in various fields dealing with time series. The software provides numerous automatic procedures to handle common practical situations, but at the same time, readers with programming skills can write their own programs to deal with specific problems. Although the theoretical introduction to each topic is kept to a minimum, readers can consult the companion book ‘Multivariate Time Series With Linear State Space Structure’, by the same author, if they require more details.  Contraportada This book presents an introduction to linear univariate and multivariate time series analysis, providing brief theoretical insights into each topic, and from the beginning illustrating the theory with software examples. As such, it quickly introduces readers to the peculiarities of each subject from both theoretical and the practical points of view. It also includes numerous examples and real-world applications that demonstrate how to handle different types of time series data. The associated software package, SSMMATLAB, is written in MATLAB and also runs on the free OCTAVE platform. The book focuses on linear time series models using a state space approach, with the Kalman filter and smoother as the main tools for model estimation, prediction and signal extraction. A chapter on state space models describes these tools and provides examples of their use with general state space models. Other topics discussed in the book include ARIMA; and transfer function and structural models; as well as signal extraction using the canonical decomposition in the univariate case, and VAR, VARMA, cointegrated VARMA, VARX, VARMAX, and multivariate structural models in the multivariate case. It also addresses spectral analysis, the use of fixed filters in a model-based approach, and automatic model identification procedures for ARIMA and transfer function models in the presence of outliers, interventions, complex seasonal patterns and other effects like Easter, trading day, etc. This book is intended for both students and researchers in various fields dealing with time series. The software provides numerous automatic procedures to handle common practical situations, but at the same time, readers with programming skills can write their own programs to deal with specific problems. Although the theoretical introduction to each topic is kept to a minimum, readers can consult the companion book ‘Multivariate Time Series With Linear State Space Structure’, by the same author, if they require more details.  Biografía del autor Dr. Víctor Gómez is a statistician and technical advisor at the Spanish Ministry of Finance and Public Administrations in Madrid. His work involves statistical, econometric and, above all, time series analysis of macroeconomic data, mostly in connection with short-term economic analysis. More recently, he has focused on research in the field of time series analysis and the development of software for time series analysis. He has also taught numerous courses on time series analysis and related topics such as short-term forecasting, seasonal adjustment methods and time series filtering.

Linear time series with matlab and octave springerlink the associated software package, ssmmatlab, is written in matlab and also runs on the free octave platform the book focuses on linear time series models using a state space approach, with the kalman filter and smoother as the main tools for model estimation, prediction and signal extraction Gnu octave bugs bug 47032, missing matlab datetime it also includes redistributions of the iana time zone database public domain and the unicode cldr windowszonesxml file unicode license i think these are compatible with octaves gpl licensing i have a matlab license but this work was produced entirely with octave, other free software, and reference to publicly available documentation Linear time series with matlab and octave víctor gómez this book provides a theoretical and practical introduction to linear univariate and multivariate time series analysis with a focus on time series models using a state space approach from the beginning, it includes software examples in matlab that illustrate the theory

Time series regression models matlab amp simulink multiple linear regression models assume that a response variable is a linear combination of predictor variables, a constant, and a random disturbance if the variables are time series processes, then classical linear model assumptions, such as spherical disturbances, might not hold Create timeseries object matlab time series represent the timeevolution of a dynamic population or process they are used to identify, model, and forecast patterns and behaviors in data that is sampled over discrete time intervals Linear time series with matlab and octave download linear time series with matlab and octave by vгctor gгіmez 2019 english pdf, epub read online 205 mb download this book presents an introduction to linear univariate and multivariate time series analysis, providing brief theoretical insights into each topic, and from the beginning illustrating the theory with software examples

Detalles del Libro

• Name: Linear Time Series with MATLAB and OCTAVE (Statistics and Computing)
  
• Autor: Víctor Gómez
  
• Categoria: Libros,Ciencias, tecnología y medicina,Matemáticas
  
• Tamaño del archivo: 11 MB
  
• Tipos de archivo: PDF Document
  
• Idioma: Español
  
• Archivos de estado: AVAILABLE
  

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A practical time series tutorial with matlab a practical time series tutorial with matlab michalis vlachos ibm tj watson research center hawthorne, ny, 10532 tutorial timeseries with matlab 2 about this tutorial the goal of this tutorial is to show you that timeseries research or research in general can be made fun, when it involves visualizing ideas, that can be achieved with Time series objects matlab amp simulink mathworks india a timeseries object contains data and time information within its properties that describes a dynamic process you can use timeseries object functions to create, modify, and analyze the behavior of a time series consider using timetables instead of timeseries objects, where you can store timestamped data as columnoriented data variables Fechas y horas matlab amp simulink mathworks españa los tipos de datos de fecha y hora datetime, duration y calendarduration admiten cálculos eficientes, comparaciones y visualizaciones con formato de fechas y horas trabaje con estos arreglos de la misma forma que trabaja con los arreglos numéricos puede agregar, sustraer, ordenar, comparar, concatenar y representar gráficamente valores de fecha y hora

Linear time series with matlab and octave bookshare linear time series with matlab and octave 1st ed 2019 statistics and computing view larger image by víctor gómez synopsis this book presents an introduction to linear univariate and multivariate time series analysis, providing brief theoretical insights into each topic, and from the beginning illustrating the theory with software Time series analysis matlab amp simulink workflow for forecasting time series data and inputoutput data using linear and nonlinear models forecast multivariate time series this example shows how to perform multivariate time series forecasting of data measured from predator and prey populations in a prey crowding scenario time series prediction and forecasting for prognosis this Time series regression i linear models matlab subsequent examples in this series consider linear regression models, built from a small set of potential predictors and calibrated to a rather small set of data still, the techniques, and the matlab toolbox functions considered, are representative of typical specification analyses