Time series in economics and finance / Tomáš Cipra.

Includes bibliographical references and index.

Introduction -- Random processes -- Trend analysis -- Seasonality and periodicity -- Residual components -- Box-Jenkins methodology -- Regression models -- Financial time series -- Volatility modeling -- Value at risk -- Multivariate time series -- State space models.

This book presents a comprehensive introduction to the analysis and forecasting of economic and financial time series using modern statistical and econometric methods. The text explains the theoretical foundations and practical applications of time-dependent data modeling in economics, finance, and business analytics. Major topics include decomposition methods, trend and seasonal analysis, autoregressive and moving-average models, Box-Jenkins methodology, regression techniques, volatility and risk modeling, financial asset dynamics, multivariate time series, cointegration, recursive state space methods, and forecasting procedures. Emphasizing practical applicability, the author combines mathematical explanations with real-world datasets, computational techniques, numerical examples, and exercises that illustrate how time series methods are applied in economic forecasting, financial analysis, and quantitative decision-making. Intended for students, researchers, and professionals, the work serves both as a textbook in econometrics and computational finance and as a reference source for advanced time series analysis.