DATAX502

This graduate-level course provides an in-depth exploration of time series analysis and stochastic modeling techniques, focusing on theoretical foundations and practical applications. Topics include time series decomposition, stationarity, autoregressive and moving average models (ARMA/ARIMA), seasonal adjustments, spectral analysis, and forecasting methods. The stochastic modeling component covers Markov processes, Brownian motion, and applications in fields like finance, econometrics, and environmental science. Emphasis is placed on model estimation, validation, and interpretation using modern computational tools. Students will apply concepts through projects and real-world data analysis.