MATH 458 - Time Series

Credits: 4

The course introduces the student to the statistical analysis of time series data. The covered topics include: autocorrelation; stationarity, basic time series models; autoregressive (AR), moving-average (MA) and ARMA; trend removal and seasonal adjustment; invertibility; spectral analysis; estimation, data analysis and forecasting with time series models; forecast errors and confidence intervals; introduction to financial time series and autoregressive conditional heteroskedasticity (ARCH) models The materials will partially cover the syllabus of SOA Exam Statistics for Risk Modeling and that of Exam Predictive Analytics. Prerequisites: C or better in MATH 329 and MATH 448, or consent of instructor. Must have junior or senior standing.