Haipeng Xing is a professor in applied mathematics and statistics at the State University of New York, Stony Brook, USA, the author of three books and numerous research papers. His research interests include quantitative finance and risk management, econometrics, applied stochastic control, and sequential statistical methodology.Chris Chatfield is a retired reader in statistics at the University of Bath, UK, the author of five books and numerous research papers, and an elected senior fellow of the International Institute of Forecasters.

• 1. Introduction. 2. Basic Descriptive Techniques. 3. Some Linear Time Series Models. 4. Fitting Time Series Models in the Time Domain. 5. Forecasting. 6. Stationary Processes in the Frequency Domain. 7. Spectral Analysis. 8. Bivariate Processes. 9. Linear Systems. 10. State-Space Models and the Kalman Filter. 11. Non-Linear Models. 12. Volatility Models. 13. Multivariate Time Series Modelling. 14. Predictive AI for Time Series. 15. Generative AI for Time Series.

Haipeng Xing is a professor in applied mathematics and statistics at the State University of New York, Stony Brook, USA, the author of three books and numerous research papers. His research interests include quantitative finance and risk management, econometrics, applied stochastic control, and sequential statistical methodology.Chris Chatfield is a retired reader in statistics at the University of Bath, UK, the author of five books and numerous research papers, and an elected senior fellow of the International Institute of Forecasters.

• 1. Introduction. 2. Basic Descriptive Techniques. 3. Some Linear Time Series Models. 4. Fitting Time Series Models in the Time Domain. 5. Forecasting. 6. Stationary Processes in the Frequency Domain. 7. Spectral Analysis. 8. Bivariate Processes. 9. Linear Systems. 10. State-Space Models and the Kalman Filter. 11. Non-Linear Models. 12. Volatility Models. 13. Multivariate Time Series Modelling. 14. Predictive AI for Time Series. 15. Generative AI for Time Series.

Haipeng Xing is a professor in applied mathematics and statistics at the State University of New York, Stony Brook, USA, the author of three books and numerous research papers. His research interests include quantitative finance and risk management, econometrics, applied stochastic control, and sequential statistical methodology.Chris Chatfield is a retired reader in statistics at the University of Bath, UK, the author of five books and numerous research papers, and an elected senior fellow of the International Institute of Forecasters.

• 1. Introduction. 2. Basic Descriptive Techniques. 3. Some Linear Time Series Models. 4. Fitting Time Series Models in the Time Domain. 5. Forecasting. 6. Stationary Processes in the Frequency Domain. 7. Spectral Analysis. 8. Bivariate Processes. 9. Linear Systems. 10. State-Space Models and the Kalman Filter. 11. Non-Linear Models. 12. Volatility Models. 13. Multivariate Time Series Modelling. 14. Predictive AI for Time Series. 15. Generative AI for Time Series.