Univariate Time Series Modeling and Forecasting with EViews

Understanding historical data trends is essential for making accurate business and economic predictions. This text-based course introduces you to the core principles of univariate time series analysis using EViews. You will transition from understanding basic statistical terms to confidently building, testing, and evaluating your own univariate forecasting models. Through clear written explanations and step-by-step software walkthroughs, you will learn how to interpret data patterns and project future values. What you'll learn: - Understand foundational time series concepts, including stationarity, trend, and seasonality. - Import, clean, and structure single-variable datasets within the EViews environment. - Identify data patterns using autocorrelation and partial autocorrelation functions. - Build and estimate autoregressive, moving average, and ARIMA models. - Evaluate model performance using diagnostic tests and selection criteria like AIC and BIC. - Generate and interpret systematic forecasts for business and economic planning. The course starts with essential terminology and statistical foundations before guiding you through data preparation, model estimation, and diagnostic checking in EViews. You will wrap up by learning how to evaluate forecast accuracy and present your findings clearly. This course is designed for beginners, students, and analysts looking to learn time series modeling without prior experience in EViews or advanced econometrics. Start reading today to unlock the predictive power of your historical data.