Author: Agustín MaravallPublisher:
ISBN:
Category : Outliers (Statistics)
Languages : en
Pages : 64
Book Description
Missing Observations and Additive Outliers in Time Series Models
Author: Agustín MaravallPublisher:
ISBN:
Category : Outliers (Statistics)
Languages : en
Pages : 64
Book Description
Missing observations and additive outliers in time series models
Author: Agustín MaravalMissing Observations and Additive Outliers in Time Series Models
Author: Missing Observations and Additive Outliers Im Time Series Models
Author: Agustín MaravallMissing Observations and Additive Outliers in Time Series Models
Author: Agustín Maravall HerreroPublisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
Program TRAMO "Time Series Regression with ARIMA Noise, Missing Observations, and Outliers" Instructions for the User
Author: Víctor GómezPublisher:
ISBN:
Category : Econometrics
Languages : en
Pages : 48
Book Description
A Course in Time Series Analysis
Author: Daniel PeñaPublisher: John Wiley & Sons
ISBN: 1118031229
Category : Mathematics
Languages : en
Pages : 494
Book Description
New statistical methods and future directions of research in time series A Course in Time Series Analysis demonstrates how to build time series models for univariate and multivariate time series data. It brings together material previously available only in the professional literature and presents a unified view of the most advanced procedures available for time series model building. The authors begin with basic concepts in univariate time series, providing an up-to-date presentation of ARIMA models, including the Kalman filter, outlier analysis, automatic methods for building ARIMA models, and signal extraction. They then move on to advanced topics, focusing on heteroscedastic models, nonlinear time series models, Bayesian time series analysis, nonparametric time series analysis, and neural networks. Multivariate time series coverage includes presentations on vector ARMA models, cointegration, and multivariate linear systems. Special features include: Contributions from eleven of the worldâ??s leading figures in time series Shared balance between theory and application Exercise series sets Many real data examples Consistent style and clear, common notation in all contributions 60 helpful graphs and tables Requiring no previous knowledge of the subject, A Course in Time Series Analysis is an important reference and a highly useful resource for researchers and practitioners in statistics, economics, business, engineering, and environmental analysis. An Instructor's Manual presenting detailed solutions to all the problems in he book is available upon request from the Wiley editorial department.
Forecasting, Structural Time Series Models and the Kalman Filter
Author: Andrew C. HarveyPublisher: Cambridge University Press
ISBN: 1107717140
Category : Business & Economics
Languages : en
Pages : 578
Book Description
In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.
Missing Observations, Additive Outliers and Inverse Autocorrelation Function
Author: Agustín MaravallPublisher:
ISBN:
Category : Autocorrelation (Statistics)
Languages : en
Pages : 56
Book Description
Nonlinear Time Series Analysis
Author: Holger KantzPublisher: Cambridge University Press
ISBN: 9780521529020
Category : Mathematics
Languages : en
Pages : 390
Book Description
The paradigm of deterministic chaos has influenced thinking in many fields of science. Chaotic systems show rich and surprising mathematical structures. In the applied sciences, deterministic chaos provides a striking explanation for irregular behaviour and anomalies in systems which do not seem to be inherently stochastic. The most direct link between chaos theory and the real world is the analysis of time series from real systems in terms of nonlinear dynamics. Experimental technique and data analysis have seen such dramatic progress that, by now, most fundamental properties of nonlinear dynamical systems have been observed in the laboratory. Great efforts are being made to exploit ideas from chaos theory wherever the data displays more structure than can be captured by traditional methods. Problems of this kind are typical in biology and physiology but also in geophysics, economics, and many other sciences.