Time Series Analysis: Forecasting and ControlTable of Contents Preface 1 Introduction 1 2 Autocorrelation Function and Spectrum of Stationary Processes 21 3 Linear Stationary Models 46 4 Linear Nonstationary Models 89 5 Forecasting 131 6 Model Identification 183 7 Model Estimation 224 8 Model Diagnostic Checking 308 9 Seasonal Models 327 10 Transfer Function Models 373 11 Identification, Fitting, and Checking of Transfer Function Models 407 12 Intervention Analysis Models and Outlier Detection 462 13 Aspects of Process Control 483 Collection of Tables and Charts 533 Collection of Time Series Used for Examples in the Text and in Exercises 540 References 556 Exercises and Problems 569 Index 589. |
Contents
INTRODUCTION AND SUMMARY | 1 |
STOCHASTIC MODELS AND THEIR | 21 |
LINEAR STATIONARY MODELS | 46 |
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a₁ approximate ARIMA ARMA autocorrelation function autocovariance autoregressive operator autoregressive process behavior calculation Chapter coefficients computed conditional expectations consider control action control equation control scheme correlation function covariance cross correlation cross covariance deviation difference equation differencing discrete distribution dynamic estimated autocorrelations example exponentially feedforward first-order fitted forecast errors given Hence identification impulse response initial estimates input invertibility iteration least squares estimates likelihood likelihood function linear matrix mean square error moving average process nonstationary observations obtained optimal output p₁ parameters partial autocorrelation partial autocorrelation function periodogram process of order recursive residuals second-order Section shows spectrum standard error starting values stationary stationary process stochastic model stochastic process sum of squares Suppose Table transfer function model unit circle variable variance viscosity Vz₁ w₁ weights white noise x₁ z₁ zero λο σα