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STAT 427. Applied Time Series

Credits: 3
Department: Statistics
Description: A study of the most useful techniques of analysis and forecasting using time series data. Topics include an introduction to forecasting, time series regression, decomposition methods, smoothing, smoothing techniques, basic techniques of Box-Jenkins methodology; use of statistical software.
Prerequisites: STAT 321
Semester Offered: DEMAND
Grading Method: ABCDF

Student Learning Outcomes

1. Derive autocorrelation functions for stationary time series such as AR and MA processes.
2. Select appropriate time series models in the ARIMA family for time series data in different situations.
3. Diagnose the fitting of an ARIMA model to a time series and forecast future values of the time series.
4. Interpret analysis results and deliver findings with a written report.
5. Use R or other software to analyze time series data, including the plots of sample autocorrelation function, sample partial autocorrelation function, and extended autocorrelation function.






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