Lecture
This lecture covers the fundamentals of time series analysis, including the data structure of time series, simple techniques, linear processes, spectral estimation, and vector-valued processes. It also delves into practical aspects such as integrated and seasonal models, correlation, model choice, forecasting, and long memory in financial time series. The instructor emphasizes the importance of stationarity, covariance matrices, autocovariance functions, and weak stationarity criteria. Various models like moving average, autoregressive, and autoregressive moving average processes are discussed, along with practical examples. The lecture concludes with trend removal, seasonality adjustment, and the impact of different processes on the observed data.