Time series analysis: forecasting and control
Stochastic models and their forecasting. The autocorrelation function and spectrum. Linear stationary models. Linear nonstationary models. Forecasting. Stochastic model building. Model estimation. Model diagnostic checking. Seasonal models. Transfer function model building. Transfer function models. Identification, fitting, and checking of transfer function models. Design of discrete control schemes. Design of feedforward and feedback control schemes. Some further problems in control.
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INTRODUCTION AND SUMMARY
STOCHASTIC MODELS AND THEIR
LINEAR STATIONARY MODELS
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added noise adjustment approximate ARIMA ARMA auto autoregressive operator autoregressive process behavior calculation Chapter chart coefficients computed conditional expectations consider control action control equation control scheme convergence correlation function corresponding covariance cross correlation cross covariance deviation diagnostic checking difference equation differencing discrete distribution dynamic estimated autocorrelations example exponentially first-order fitted follows forecast errors given Hence identification illustrate impulse response initial estimates input invertibility iteration lead least squares estimates likelihood function linear matrix mean square error minimum mean square moving average process nonstationary observations obtained optimal output partial autocorrelation function particular periodogram plotted process of order Program quadratic random recursive residuals roots sampling interval second-order Section shows spectrum standard error starting values stationary process stochastic model stochastic process substituting sum of squares Suppose Table transfer function model unit circle variable variance viscosity weights white noise zero