Time Series Analysis: Forecasting and Control
This is a complete revision of a classic, seminal, and authoritative book that has been the model for most books on the topic written since 1970. It focuses on practical techniques throughout, rather than a rigorous mathematical treatment of the subject. It explores the building of stochastic (statistical) models for time series and their use in important areas of application —forecasting, model specification, estimation, and checking, transfer function modeling of dynamic relationships, modeling the effects of intervention events, and process control. Features sections on: recently developed methods for model specification, such as canonical correlation analysis and the use of model selection criteria; results on testing for unit root nonstationarity in ARIMA processes; the state space representation of ARMA models and its use for likelihood estimation and forecasting; score test for model checking; and deterministic components and structural components in time series models and their estimation based on regression-time series model methods.
What people are saying - Write a review
We haven't found any reviews in the usual places.
Stochastic Models and Their Forecasting
Link Between the Sample Spectrum
22 other sections not shown
adjustment Appendix approximate ARIMA model ARMA ARMA(p autocovariance autoregressive operator autoregressive process behavior calculation Chapter chart coefficients computed conditional expectations consider corresponding covariance matrix cross correlation cross covariance deterministic deviation difference equation differencing discrete distribution dynamic estimated autocorrelations example exponentially first-order fitted follows forecast errors forecast function given Hence identified illustrate impulse response input interval invertibility iterative lead least squares estimates likelihood function linear mean square error methods minimum mean square moving average process nonstationary observations obtained outliers output parameters partial autocorrelation function particular periodogram procedure process of order quadratic random recursively regression represented residuals roots sample scheme seasonal second-order Section shows spectrum square error forecast standard error starting values stationary process stochastic process substituting sum of squares Suppose Table tion transfer function model unit circle updating variable variance vector weights white noise z,+i zero