STAT 350 Time Series Analysis

This course will cover models for analyzing time series data from both time and frequency domain perspectives. The emphases will be a balance of theory and applications. The course is intended to prepare the student for methodological research in this area and to train the students on cutting-edge data analytic methods for time series. The primary topics include ARMA/ARIMA models; spectral and coherence estimation; transfer function modeling; and classification and discrimination of time series. The course will conclude with advanced topics on non-stationary time series, time-frequency analysis and state-space models.




STAT 220, STAT 230, STAT 240, STAT 250 (Please note: prerequisites are for M.Sc. students only) or Approval by course instructor