Introduction -- Typical assumptions -- Defining probability measure for time series -- Spectral representation of univariate time series -- Spectral representation of real valued vector time series -- Univariate ARMA processes -- Generalized autoregressive processes -- Prediction -- Inference for μ, γ and F -- Parametric estimation -- References.

This book provides a concise introduction to the mathematical foundations of time series analysis, with an emphasis on mathematical clarity. The text is reduced to the essential logical core, mostly using the symbolic language of mathematics, thus enabling readers to very quickly grasp the essential reasoning behind time series analysis. It appeals to anybody wanting to understand time series in a precise, mathematical manner. It is suitable for graduate courses in time series analysis but is equally useful as a reference work for students and researchers alike.

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