Forecasting
Undergraduate · Statistics
Syllabus focus
Standard syllabus · STEM / applied
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Topics typically covered
Standard syllabus
Forecasting foundations
- Components of time series: trend, seasonality, cycle
- Naive, average, and seasonal naive benchmarks
- Moving averages and exponential smoothing
- Forecast accuracy metrics: MAPE, MAD, RMSE
- Holdout samples and rolling forecasts
Regression-based forecasting
- Trend models and polynomial regression
- Seasonal dummy variables
- Autoregressive forecasting models (intro)
- Leading indicators and causal forecasting
- Combining forecasts
Judgment and communication
- Role of judgmental adjustments
- Forecast intervals and scenario analysis
- Forecasting for inventory and demand planning
- Presenting forecasts to decision makers
STEM / applied
Applied forecasting tools
- Forecasting in Excel, R, or Python
- Hierarchical forecasting (introduction)
- Forecasting competitions and M-competition insights
- Real-time forecasting dashboards
- Handling promotions and external shocks
- Case studies in retail, energy, and finance
Additional applied practice
- Reviewing assumptions with domain experts
- Documenting analysis choices for reproducibility
- Sensitivity analyses for key modeling decisions
- Connecting results to the original research or business question
Notes
Distinct from full time series theory courses; emphasizes practical prediction workflows. Often taught in business statistics programs.