Time series analysis
Undergraduate · Statistics
Syllabus focus
Standard syllabus · STEM / applied
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$60.00 · 60 min · Undergraduate · Online ($60/hr)
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Topics typically covered
Standard syllabus
Time series fundamentals
- Trend, seasonality, and stationarity
- Autocorrelation and partial autocorrelation functions
- White noise and random walks
- Differencing and detrending
- Decomposition: classical and STL (intro)
ARIMA models
- Autoregressive (AR) and moving average (MA) models
- ARMA and ARIMA model identification
- Model selection with AIC and BIC
- Forecasting with ARIMA models
- Prediction intervals for future observations
Additional topics
- Seasonal ARIMA (SARIMA) models
- Exponential smoothing methods
- Unit root tests (introduction)
- Spectral analysis overview (optional)
STEM / applied
Applied forecasting
- Forecasting with R (forecast package) or Python
- Evaluating forecasts: MAPE, RMSE, and cross-validation
- Business and economic time series case studies
- Handling irregularly spaced or missing time points
- Introduction to GARCH models for volatility
- Communicating forecasts and uncertainty to stakeholders
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
Undergraduate time series courses vary from ARIMA-focused to spectral-methods light introductions. Applied sections emphasize forecasting in business and economics.