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 key analytical methods and techniques used in the analysis and forecasting of time series data. 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: 410-DL Supervised 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.


Summer 2021
Start/End DatesDay(s)TimeBuildingSection
06/21/21 - 08/29/21Optional Sync W
7 – 9:30 p.m. 55
InstructorCourse LocationStatusCAESAR Course ID
Riggs, Jamie
Online
Open
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