Zheng Ke
STAT 131 | Spring 2025 | Course Listing
Monday & Wednesday, 3:00 PM – 4:15 PM
Introduction to time series models and forecasting. Introduction to classical time series model: autoregressive, moving average, ARIMA models. Some concepts from stochastic processes: martingales, stationarity, Gaussian processes, Brownian motions, ergodic theorems. Some aspects of advanced time series: hidden Markov models, state space models, filtering, smoothing, Kalman filters, sequential Monte Carlo methods.
Recommended Prep: Statistics 111 and 139 or equivalent.