Cross-listed course: SDS 4155-01 and SDS 5155-01 meet together. Class meetings: Tue/Thu 10:00–11:20 a.m. in MCMILLIN X, Room 0G052. Midterm: Tuesday, October 13. Thursday, October 8 is a self-review day with no class. Topic pacing may be adjusted during the semester.
| Week | Class dates | Topics and milestones | Textbook sections | Lecture notes |
|---|---|---|---|---|
| 1 · Aug 24–28 | Tue Aug 25Thu Aug 27 | Data integrity and cleaning: time indexes, duplicates, gaps, missing values, units, and outliers; moving from a general series toward a stationary representation. Stationarity and its basic properties. | S&S §§1.1, 1.3–1.4, 2.2 | 1. Cleaning and the road to stationarity 2. Stationary time series |
| 2 · Aug 31–Sep 4 | Tue Sep 1Thu Sep 3 | Diagnosing nonstationarity: time plots, sample ACF, white noise, trends, seasonality, and random walks. Transformations, detrending, seasonal adjustment, and differencing; a complete case from raw data to a stationary representation. | S&S §§1.2, 1.5, 2.1–2.2 | 3. Diagnosing nonstationarity 4. Obtaining a stationary representation |
| 3 · Sep 7–11 | Tue Sep 8Thu Sep 10 | Moving-average models MA(q); autoregressive models AR(p); stationarity conditions, ACF/PACF patterns, simulation, and simple prediction. | S&S §§1.2, 3.1–3.3 | 5. Moving-average models MA(q) 6. Autoregressive models AR(p) |
| 4 · Sep 14–18 | Tue Sep 15Thu Sep 17 | ARMA models; causal and invertible representations; model identification using ACF and PACF. | S&S §§3.1–3.3 | Lectures 7–8 |
| 5 · Sep 21–25 | Tue Sep 22Thu Sep 24 | Estimation and likelihood for ARMA models; AIC and model comparison; residual diagnostics and model adequacy. | S&S §§3.5, 3.7 | Lectures 9–10 |
| 6 · Sep 28–Oct 2 | Tue Sep 29Thu Oct 1 | Forecast construction and uncertainty; integrated ARIMA models and practical differencing; introduction to seasonal ARIMA. | S&S §§3.4, 3.6, 3.9 | Lectures 11–12 |
| 7 · Oct 5–9 | Tue Oct 6 · Fall BreakThu Oct 8 · Self-review; no class | Fall Break Oct 3–6 Students use the posted review materials to prepare independently for the midterm. |
Pre-midterm material | Self-review materials distributed in Canvas |
| 8 · Oct 12–16 | Tue Oct 13 · MidtermThu Oct 15 · Class | Midterm Tuesday, 10:00–11:20 a.m., MCMILLIN X, Room 0G052. Dynamic regression and regression with autocorrelated errors begin Thursday. |
Pre-midterm material; S&S §§3.8, 5.5 | Review materials; Lecture 13 |
| 9 · Oct 19–23 | Tue Oct 20Thu Oct 22 | Forecast evaluation and time-series cross-validation; exponential smoothing and ETS models. | S&S §3.4; selected notes | Lectures 14–15 |
| 10 · Oct 26–30 | Tue Oct 27Thu Oct 29 | State-space models, filtering, smoothing, forecasting, and the Kalman filter. | S&S §§6.1–6.3 | Lectures 16–17 |
| 11 · Nov 2–6 | Tue Nov 3Thu Nov 5 | Frequency-domain motivation; cyclical behavior, spectral density, the periodogram, and spectral smoothing. | S&S §§4.1–4.4 | Lectures 18–19 |
| 12 · Nov 9–13 | Tue Nov 10Thu Nov 12 | Volatility and ARCH/GARCH models; change-point detection and structural breaks. | S&S §5.3; selected notes | Lectures 20–21 |
| 13 · Nov 16–20 | Tue Nov 17Thu Nov 19 | A nontechnical survey of regularization, tree-based forecasting, and neural approaches; probabilistic forecasts and forecast combinations. | Selected notes and readings | Lectures 22–23 |
| 14 · Nov 23–27 | Tue Nov 24 · ClassThu Nov 26 · Thanksgiving Break | Cross-correlation and an introduction to vector autoregression. Thanksgiving Break Nov 25–29 | S&S §§1.3, 1.6, 5.6 | Lecture 24 |
| 15 · Nov 30–Dec 4 | Tue Dec 1Thu Dec 3 · Last meeting | Integrated capstone case; course synthesis, model-selection workflow, and cumulative final review. | Selected notes; cumulative review | Lectures 25–26 |
| Finals · Dec 10–16 | Exact date, time, and location assigned by the university | Cumulative final examination | Cumulative | Practice materials distributed in Canvas |
Calendar anchors: first day of classes August 24; Labor Day September 7; Fall Break October 3–6; Thanksgiving Break November 25–29; last day of classes December 7; reading days December 8–9; final exams December 10–16.