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STAT 645 Time Series Forecasting 3.0 Credits
This course provides a comprehensive introduction to the latest time series forecasting methods. Topics such as autocorrelation, forecast accuracy, seasonality, stationarity, decomposition, time series linear models, exponential smoothing, and ARIMA models are discussed. The course provides a practical skillset to students interested in more accurately forecasting future energy usage, retail sales, crime, economic indicators, user engagement, or any data which is repeatedly measured over time. Knowledge of a statistical programming language is prerequisite.
Repeat Status: Not repeatable for credit
Prerequisites: STAT 610 [Min Grade: C]
