Statistical and Data Sciences 391 - Seminar: Time Series

Offered as MTH 391 and SDS 391. Time series refers to datasets where there is a sequential order for the observations. The primary objective of time series analysis is to develop mathematical models that characterize the relationship of observed time series data. Topics in this course include perspectives from linear regression (time dependent covariates or errors), nonparametric techniques (smoothing, moving averages, nearest neighbors), and time domain models (autoregressive and moving average models, and their extensions). In addition, the course concludes with an introduction to an advanced topic, for example: count time series, change-point detection, spectral (fourier) analysis, longitudinal, or spatial-temporal relationships. Prerequisite: MTH 112, SDS 100 and (SDS 291 or ECO 240.) Restrictions: Juniors and seniors only. Enrollment limited to 12. Instructor permission required.

[CE] JR/SR only; Prereq: MTH 112, SDS 100, & (SDS 291 or ECO 240)