000			01872nam a2200217Ia 4500
005			20260630100029.0
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020			_a9781584889212
082			_a519.55 KIT _223
100			_aKitagawa, Genshiro.
245		0	_aIntroduction to Time Series Modeling _c/ Genshiro Kitagawa.
250			_aFirst edition.
260			_aBoca Raton, FL _b: Chapman & Hall/CRC Press _c, 2010.
300			_axxiii, 289 p. _b: illustrations _c; 25 cm.
520			_aIntroduction to Time Series Modeling by Genshiro Kitagawa is a comprehensive textbook that presents the theory and practical techniques of modern time series analysis. The book explains how observed data collected over time can be modeled to identify underlying structures, make forecasts, and support decision-making. It provides a unified treatment of both classical time series methods and modern state-space modeling techniques. The text begins with the fundamentals of descriptive time series analysis, covariance functions, spectral analysis, and statistical modeling. It then introduces widely used models such as autoregressive (AR), moving average (MA), and autoregressive moving average (ARMA) models before extending these concepts to state-space models. A major emphasis is placed on recursive estimation techniques, including the Kalman filter, smoothing algorithms, and sequential Monte Carlo methods, which are essential for analyzing dynamic systems and handling noisy observations. The author also discusses trend estimation, seasonal adjustment, locally stationary models, non-Gaussian state-space models, simulation techniques, and model selection using the Akaike Information Criterion (AIC).
650			_aMathematical statistics.
650			_aState-space methods.
650			_aStatistical models.
650			_aTime-series analysis.
942			_cVC OFFICE
999			_c69834 _d69834