Time series data types, autocorrelation, stationarity and nonstationarity, autoregressive moving average models, model selection methods, bootstrap confidence intervals, trend and seasonality, forecasting, nonlinear time series, filtering and smoothing, autoregressive conditional heteroscedasticity models, multivariate time series, vector autoregression, frequency domain, spectral density, state-space models, Kalman filter. Emphasis on real-world applications and data analysis using statistical software.
Prerequisite: Math/SDS 493 or Math/SDS 3211, Math/SDS 3200, Math/SDS 494 or Math/SDS 4211.
Course Attributes: FA NSM; AS NSM
