Course

Course Summary
Credit Type:
Course
ACE ID:
STAT-0051
Organization's ID:
#510
Organization:
Statistics.com
Location:
Online
Length:
4 weeks (60 hours)
Dates Offered:
Credit Recommendation & Competencies
Level Credits (SH) Subject
Upper-Division Baccalaureate 3 Forecasting Analytics
Description

Objective:

The course objective is to teach students how to choose an appropriate time series forecasting method, fit the model, evaluate its performance, and use it for forecasting. The course focuses on the most popular business forecasting methods: regression models, smoothing methods including Moving Average (MA) and Exponential Smoothing, and Autoregressive (AR) models. It also discusses enhancements such as second-layer models and ensembles, and various issues encountered in practice.

Learning Outcomes:

  • Visualize time series data.
  • Distinguish explanation from forecasting.
  • Use regression methods for forecasting.
  • Distinguish real trend and patterns from random behavior.
  • Understand the different components of time series data.
  • Specify appropriate metrics to assess forecasting models.
  • Use smoothing methods with time series data (moving average, exponential smoothing).
  • Adjust for seasonality.
  • Account for autocorrelation.

General Topics:

  • Characterizing Time Series and the Forecasting Goal
  • Smoothing-based Methods
  • Regression-based Models
  • Forecasting in Practice
Instruction & Assessment

Instructional Strategies:

  • Audio Visual Materials
  • Discussion
  • Lectures
  • Practical Exercises
  • Textbook readings

Methods of Assessment:

  • Other
  • Capstone case study project, graded practical exercises, and discussion forums

Minimum Passing Score:

80%
Supplemental Materials