This course introduces students to the theory and practice of time series analysis, focusing on statistical methods for analysing data collected sequentially over time. Students learns techniques for identifying patterns, modelling temporal dependence, forecasting future observations, and evaluating the performance of forecasting models. The course covers smoothing techniques, classical decomposition methods, and stochastic time series models, including Autoregressive (AR), Moving Average (MA), Autoregressive Moving Average (ARMA), and Box–Jenkins models. Students also study the concepts of stationarity, autocorrelation, and white noise processes, as well as methods for testing stationarity. Practical applications using statistical software enable students to analyse real-world time series data and develop forecasting models applicable to business, economics, finance, environmental science, and other disciplines.