This course provides an introduction to statistical time series analysis. The course will cover both the theoretical and practical aspects of time series, but will concentrate more on practical matters. We will cover the following topics.
Basic Theory : Vector space theory, stochastic processes, stationarity, prediction.
Time Domain Theory : time domain parameters, autoregressive, moving-average and mixed processes.
Model Building and Forecasting : model selection, fitting, prediction, non-stationarity and seasonality, goodness-of-fit tests.
Frequency Domain Theory : spectral representations, linear filtering, spectral and cross-spectral estimation, model fitting.
The course will focus on developing the theoretical intuition and practical skills to actually carry out analyses of time series data. I will make a some use of non-trivial mathematical methods, but these will be reviewed as we need them.
The class is scheduled to meet Monday, Wednesday and Friday 10am-11am in room B08.
The final grade will be based on a combination of assignments (40%) and a final exam (60%).