The Analysis of Time Series: An Introduction |
Contents
Simple descriptive techniques | 9 |
Probability models for time series | 27 |
Estimation in the time domain | 49 |
Copyright | |
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Common terms and phrases
acv.f alternative approach appropriate ARIMA model ARMA autocorrelation coefficient autocorrelation function autocovariance function autoregressive Box and Jenkins Box-Jenkins calculate called Chapter Chatfield component consider constant correlation correlogram cross-correlation cross-spectrum defined denote described deterministic differenced series different from zero discrete distribution equation error example exponential smoothing Figure first-order fitted Fourier transform frequency response function given time series Holt-Winters impulse response function integral intervals Jenkins and Watts Kalman filter Laplace transform linear system methods MINITAB moving average multiple multivariate noise non-stationary Nyquist frequency parameters partial ac.f Parzen Parzen window periodogram phase plot power spectral density problem properties purely random process random variables residuals sample seasonal effect seasonal variation Section sinusoidal spectral analysis spectral density function state-space models stationary process statistical stochastic process sum of squares time-series analysis trend and seasonal Tukey window univariate values variance vector X₁