Introduction to Time Series and Forecasting

Foundations of time series analysis and forecasting, bridging classical statistical methods with modern artificial intelligence. Covers stationarity, ARIMA/SARIMA models, volatility modeling (GARCH), and multivariate systems (VAR/Cointegration). Introduces deep learning architectures including RNNs, LSTMs, and Transformers for temporal data. Emphasis on practical implementation using Python for financial, biomedical, and environmental applications. Recommended prerequisite: Basic calculus, linear algebra, introductory statistics, and proficiency in Python programming.