Course Descriptions and Schedule

Please note that course schedules may be amended due to low enrollment, faculty availability, and/or other factors.

MSDS 413-DL : Time Series Analysis and Forecasting


Description

This course covers analytical methods for time series analysis and forecasting. Specific topics include the role of forecasting in organizations, exploratory data analysis, stationary and non-stationary time series, autocorrelation and partial autocorrelation functions, univariate autoregressive integrated moving average (ARIMA) models, seasonal models, Box-Jenkins methodology, regression models with ARIMA errors, multivariate time series analysis, and non-linear time series modeling including exponential smoothing methods, random forest analysis, and hidden Markov modeling.

Recommended prior course: MSDS 410-DL Supervised Learning Methods and MSDS 411-DL Unsupervised Learning Methods

Prerequisites: (1) MSDS 420-DL Database Systems and Data Preparation or CIS 417 Database Systems Design and Implementation and (2) MSDS 422-DL Practical Machine Learning or CIS 435 Practical Data Science Using Machine Learning.


Fall 2022
Start/End DatesDay(s)TimeBuildingSection
09/20/22 - 12/10/22Optional Sync W
7 – 9:30 p.m. 55
InstructorCourse LocationStatusCAESAR Course ID
Riggs, Jamie
Online
Open

Spring 2023
Start/End DatesDay(s)TimeBuildingSection
03/27/23 - 06/10/23Optional Sync W
7 – 9:30 p.m. 55
InstructorCourse LocationStatusCAESAR Course ID
Riggs, Jamie
Online
Open
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