The Analysis of Time Series: An Introduction
This book provides a comprehensive introduction to the theory and practice of time series analysis.
Topics include o ARIMA probability models o forecasting methods o spectral analysis o linear systems o state-space models o Kalman filter. Building on the success of earlier editions, the fourth edition serves as a valuable text for undergraduates and postgraduates taking courses in time series as well as provides an excellent resource for self-study.
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Simple descriptive techniques
Probability models for time series
Estimation in the time domain
11 other sections not shown
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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 F(co Figure first-order fitted Fourier transform frequency domain frequency response function give given time series H(co Holt-Winters impulse response function integral intervals Jenkins and Watts Kalman filter lag window Laplace transform linear system methods MINITAB multivariate noise non-stationary Note Nyquist frequency parameters partial acf 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 spectrum state-space models stationary process statistical stochastic process sum of squares time-series analysis trend and seasonal Tukey window univariate variance vector