Standard syllabus
Time series analysis · Undergraduate · Statistics
Topics
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)
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