Time Series

3 Credits · Level: Graduate

Time series analytics focuses on trends that occur in data over time. The Time Series class will use R to perform time series analysis for a variety of applications, including financial, econometrics, policy, health, engineering, forecasting, etc. Time series data can be used to better understand temporal forces and to generate predicative models. Often, underlying or latent effects can lead to observable trends. Analytics techniques will include model fitting, statistical methods, visualization, and storytelling. This course will include structured programming with the R language, statistical computing, the use of models to make forecasts, data formatting, cleaning and manipulation of data, solving statistical and time series equations, building predictive models, utilizing graphical applications, and applying applicable machine learning methods and models. It is recommended that students know multivariate calculus, linear algebra, and probability and statistics at the undergraduate level. **DSPP STUDENTS ONLY