Description reads as: An introduction to the theory and tools to conduct time series analysis, with the emphasis on modeling and forecasting using a software. Stationarity, white noise, autocorrelation, partial autocorrelation, and linear predictor. Stationary ARIMA models, seasonality and trends. Model fitting, diagnostics and forecasting. Additional topics may include state space models, spectral analysis of time series, and GARCH models.
This course may not be repeated for credit.
- Statistics 429 or consent of the Department.
This course will be offered next in Winter 2018