Introduction to time series analysis including autoregressive, moving average, and ARIMA models.
These are the key learning objectives for Time Series Models on SOA Exam SRM. Paraphrased from the public SOA syllabus — we recommend also checking the current syllabus on soa.org before your exam sitting.
Identify stationarity and apply differencing to achieve stationarity
Fit and interpret autoregressive, moving average, and ARIMA models
Assess time series model fit using residual diagnostics
Upload your ACTEX Exam SRM digital edition, scanned ASM pages, TIA handouts, or your own notes. exclam.ai extracts the Time Series Models sections and generates flashcards automatically.
Generate multiple-choice quizzes specifically on Time Series Models. Weak questions get re-surfaced until you get them right consistently.
Because Time Series Models is 5–10% of your exam, losing it during review costs you. FSRS brings it back at the optimal moment.
SOA Exam SRM has 5 topic areas. Time Series Models is weighted at approximately 5–10% of the exam — here is where it sits relative to the other topics.
| Topic area | Weight |
|---|---|
| Basics of Statistical Learning | 7–13% |
| Linear Models | 40–50% |
| Decision Trees | 20–25% |
| Principal Components and Cluster Analysis | 5–10% |
| → Time Series Models | 5–10% |
Supervised and unsupervised learning, model assessment, bias-variance tradeoff, and resampling methods including cross-validation.
Ordinary least squares regression, generalized linear models, variable selection, and regularization techniques.
Classification and regression trees, pruning, and ensemble methods including bagging, random forests, and boosting.
Dimension reduction via principal components analysis and unsupervised clustering via k-means and hierarchical clustering.
Upload your ACTEX Exam SRM digital edition, scanned ASM pages, TIA handouts, or your own notes. exclam.ai generates a fully guided study plan with adaptive flashcards and quizzes for this topic.