The theory and practice of applied time series analysis will be explored. First the different segments (trend, seasonality, cyclical, and irregular) of a time series will be analyzed by examining the Autocorrelation functions (ACF) and Partial Autocorrelation functions (PACF). The various types of time series models include linear regression, panel regression, seasonal decomposition, exponential smoothing, and ARIMA modeling as well as combining models.
In short, this course will equip you with tools necessary to construct forecasts to inform business decisions. As such, the focus of the course will not be only on tools, but also on how they are used in business.
STEM-designated
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