Intro -- Statistics for Astrophysics - Time Series Analysis -- Organisers -- Lecturers -- Acknowledgments -- List of Participants -- Table of Contents -- Foreword -- 1 - AN OVERVIEW OF THIS BOOK -- Foreword -- 1 An overview of the contents -- 2 Some examples of time series -- 3 Time series and Regression -- References -- 2 - TIME SERIES. FUNDAMENTALS CONCEPTS -- 1 Time series in L2. Basic notions. Stationarity -- 2 The autocovariance/autocorrelation and partial autocorrelationfunctions. -- 3 Spectral density and discrete Fourier transform -- 4 Linear, causal processes. Invertible processes. Linear forecasting -- 5 Basic statistics for a stationary process -- 6 Vector time series -- Appendix -- References -- 3 - ARMA and ARIMA TIME SERIES -- 1 Some preliminaries -- 2 AR(p), MA(q) and ARMA(p, q). Definitions and elementary properties -- 3 ARIMA series and extensions -- 4 Forecasting stationary series -- 5 Estimation -- 6 Building ARIMA models strategy -- 7 Examples -- Appendix -- References -- 4 - COMPLEMENTS and APPLICATIONS -- 1 Stationarity tests. Unit roots tests -- 2 Long memory time series -- 3 Univariate ARMAX -- 4 Transfer function models -- 5 Multivariate ARMAX -- References -- 5 - KALMAN FILTER AND TIME SERIES -- 1 Introduction. State space models -- 2 Filtering and smoothing Kalman algorithms -- 3 Parameters estimation. Maximum likelihood estimation -- 4 Miscellaneous -- References -- 6 - AN INTRODUCTION TO STATE-SPACE MODELS -- 1 Definitions and basic properties -- 2 Filtering and smoothing -- 3 Numerical methods -- 4 Endnotes -- Bibliography.