Time Series Analysis: Forecasting and ControlHolden-Day, 1976 - 575 sayfa Table 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. |
İçindekiler
PREFACE | 1 |
STOCHASTIC MODELS AND THEIR | 21 |
LINEAR STATIONARY MODELS | 46 |
Telif Hakkı | |
33 diğer bölüm gösterilmiyor
Diğer baskılar - Tümünü görüntüle
Time Series Analysis: Forecasting and Control George E. P. Box,Gwilym M. Jenkins Metin Parçacığı görünümü - 1970 |
Time Series Analysis: Forecasting and Control George E. P. Box,Gwilym M. Jenkins Metin Parçacığı görünümü - 1976 |
Time Series Analysis: Forecasting and Control George E. P. Box,Gwilym M. Jenkins Metin Parçacığı görünümü - 1970 |
Sık kullanılan terimler ve kelime öbekleri
a₁ Appendix approximate ARIMA ARMA autocorrelation function autocovariance autoregressive operator autoregressive process behavior calculation Chapter coefficients computed conditional expectations consider contours control scheme correlation function covariance cross correlation cross covariance deviation diagnostic checking difference equation differencing discrete distribution estimated autocorrelations example exponentially Figure first-order fitted forecast errors given Hence identification impulse response initial estimates input interval invertibility iteration lead least squares estimates likelihood function linear matrix mean square error minimum mean square moving average process n₁ nonstationary observations obtained optimal output p₁ parameters partial autocorrelation partial autocorrelation function particular process of order quadratic recursive residuals second-order Section shown shows square error forecast standard error starting values stationary stationary process stochastic model substituting sum of squares Suppose Table transfer function model V²z variance w₁ weights white noise X₁ Y₁ Z₁ zero Φι