Standard syllabus
Time series analysis · Undergraduate · Math
Learning objectives from the Time series analysis syllabus, grouped by unit. Click an objective for study materials.
Topics typically covered
Click a topic for the full text and related unit practice.
Undergraduate Time series analysis — outline derived from course README sections and typical US statistics syllabi (OpenIntro / standard OER where applicable).
Time series fundamentals
- Trend and seasonality — Trend and seasonality
- Stationarity — Stationarity
- Autocorrelation — Autocorrelation
- Partial autocorrelation functions — Partial autocorrelation functions
- White noise and random walks — White noise and random walks
- Differencing and detrending — Differencing and detrending
- Decomposition: classical — Decomposition: classical
- STL (intro) — STL (intro)
ARIMA models
- Autoregressive (AR) — Autoregressive (AR)
- Moving average (MA) models — Moving average (MA) models
- ARMA and ARIMA model identification — ARMA and ARIMA model identification
- Model selection with AIC and BIC — Model selection with AIC and BIC
- Forecasting with ARIMA models — Forecasting with ARIMA models
- Prediction intervals for future observations — Prediction intervals for future observations
Additional topics
- Seasonal ARIMA (SARIMA) models — Seasonal ARIMA (SARIMA) models
- Exponential smoothing methods — Exponential smoothing methods
- Unit root tests (introduction) — Unit root tests (introduction)
- Spectral analysis overview (optional) — Spectral analysis overview (optional)
Learning objectives
Click an objective for study materials.
Time series fundamentals
- Trend and seasonality — Trend and seasonality
- Stationarity — Stationarity
- Autocorrelation — Autocorrelation
- Partial autocorrelation functions — Partial autocorrelation functions
- White noise and random walks — White noise and random walks
- Differencing and detrending — Differencing and detrending
- Decomposition: classical — Decomposition: classical
- STL (intro) — STL (intro)
ARIMA models
- Autoregressive (AR) — Autoregressive (AR)
- Moving average (MA) models — Moving average (MA) models
- ARMA and ARIMA model identification — ARMA and ARIMA model identification
- Model selection with AIC and BIC — Model selection with AIC and BIC
- Forecasting with ARIMA models — Forecasting with ARIMA models
- Prediction intervals for future observations — Prediction intervals for future observations
Additional topics
- Seasonal ARIMA (SARIMA) models — Seasonal ARIMA (SARIMA) models
- Exponential smoothing methods — Exponential smoothing methods
- Unit root tests (introduction) — Unit root tests (introduction)
- Spectral analysis overview (optional) — Spectral analysis overview (optional)
Multi-Unit Problems
Course-level sets that combine skills across study units (coming soon).
Browse Multi-Unit ProblemsWhat each unit includes
Open a unit below for full materials. Typical resources:
- Study guide
- Exam Strategy
- Common Mistakes
- Worksheets
- Word problems
- Mixed Practice
- Multi-Unit Problems
- Review
- Practice test
- Answer key
Study units
Each unit includes a study guide, worksheets, review, practice test, and answer key. One unit is free; subscribe for the full class.
- Time series fundamentals
Trend and seasonality
Coming soon - ARIMA models
Autoregressive (AR)
Coming soon - Additional topics
Seasonal ARIMA (SARIMA) models
Coming soon
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$1,162 · Time series analysis · 18 tutoring hrs
Study guides, worksheets, reviews, practice tests, and answer keys for 1 class. 18 tutoring hours (1 hr / week · semester). Bundle discount applied vs buying separately. Pay in full via Zelle.