3 credits
Spring 2025 Lecture Distance Learning Upper Division
An introductory course in stationary time series with applications. Topics include stationarity, autocovariance function, ARIMA (Autoregressive Integrated Moving Average) models, and basic spectral analysis (periodograms and their estimation, tapering, linear filtering). Extensive use of R is incorporated for building time series models and estimating the time series spectrum.
Learning Outcomes1Recognize basic properties of stationary time series and various aspects and representations of ARMA and ARIMA models.
2Understand and apply standard time domain techniques for the identification, estimation, and forecasting of ARIMA models.
3Identify elementary concepts and techniques for spectral analysis.
4Use R facilities to analyze real life time series data.
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Attachments
made by Purdue students.