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Course Schedule

Data Science

Course Descriptions and Schedule

MSDS 413-DL : Time Series Analysis and Forecasting


The objective of this course is to cover key analytical techniques used in the analysis and forecasting of time series data. Specific topics include the role of forecasting in organizations, exponential smoothing methods, 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, transfer function modeling, intervention analysis, and multivariate time series analysis.

Recommended prior course: 411-DL Generalized Linear Models. 

Prerequisites: MSDS 420-DL Database Systems and Data Preparation and MSDS 422-DL Practical Machine Learning.

Winter 2019
Start/End DatesDay(s)TimeBuildingSection
01/07/19 - 03/25/19Optional Sync Th
7 – 9:30 p.m. 55
InstructorCourse FormatStatusCAESAR Course ID
Fulton, Lawrence
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