This course provides a comprehensive and hands-on introduction to univariate time series modeling with a strong focus on ARMA (AutoRegressive Moving Average) techniques using EViews software. Designed for learners with foundational statistical knowledge, the course enables participants to apply, analyze, and evaluate key components of time series analysis, from identifying autocorrelation patterns to building and diagnosing ARMA models.

In Module 1, learners are guided through the conceptual foundation of univariate time series, including the construction and interpretation of correlograms. Using real-world data, students identify time-dependent components and analyze autocorrelation structures to determine appropriate model forms. In Module 2, the focus shifts to ARMA estimation, output interpretation, and model diagnostics. Learners interpret EViews estimation results, evaluate parameter significance, and assess residual patterns using correlograms and statistical tests such as the Ljung-Box Q test. Throughout the course, practical exercises and quizzes reinforce understanding, enabling learners to develop models that are both theoretically sound and empirically valid. By course completion, participants will be able to confidently construct and validate univariate ARMA models for real-world forecasting and analytical tasks.