Statistics 131 -- Time Series Analysis and Forecasting
Course Description: An introduction to time series models and associated methods of data analysis and inference. Auto regressive (AR), moving average (MA), ARMA, and ARIMA processes, stationary and non-stationary processes, seasonal processes, auto-correlation and partial auto-correlation functions, identification of models, estimation of parameters, diagnostic checking of fitted models, forecasting, time domain regression approach including Box-Jenkins method and spectral analysis.

This class is at the Undergraduate level
Instructor: Yingying Fan
Prerequisites: Statistics 111 and 139 or equivalent.

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