A course in time series analysis
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:
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 the book is available upon request from the Wiley editorial department.
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Autocorrelation Linear Prediction
Univariate Autoregressive MovingAverage Models
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additive outlier algorithm applied approach approximation ARCH models ARIMA model ARMA Assoc assume asymptotic autocorrelation function autocovariances autoregressive model bandwidth Bayesian bilinear Box and Tiao chapter coefficients cointegrating computed consider correlation covariance criterion cycle denote detection differencing distribution Econ effect equation error example Figure forecast frequency G6mez GARCH Gibbs Gibbs sampling given innovative outliers input Kalman filter kernel level shift likelihood function linear model linear regression Maravall matrix maximum likelihood mean methods model identification model selection moving-average multivariate neural networks nonlinear nonparametric nonparametric regression nonstationary observed series obtained parameter estimates partial autocorrelation periodogram polynomial prediction problem procedure properties random regression residuals SACF sample autocorrelation seasonal adjustment seasonal component Section Series Analysis series models specification spectrum Stat state-space stationary stationary process Statistical stochastic structure threshold transfer function trend Tsay unit roots values variables vector volatility white noise zero