Introduction 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).