Evaluating Time Series Models: Goodness of Fit, AIC, and BIC

Building a time series model is only half the battle; the real challenge lies in choosing the one that forecasts accurately without overfitting. Understanding how to measure goodness of fit and apply information criteria is essential for making reliable, data-driven decisions. This written course guides you through the foundational theory and practical application of model evaluation. You will learn how to balance model accuracy with simplicity, ensuring your forecasting models perform exceptionally well on unseen future data. What you will learn: Understand the fundamental concepts of model underfitting, overfitting, and the bias-variance tradeoff; Calculate and interpret key goodness of fit metrics, including residual diagnostics; Apply Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) to compare competing models; Evaluate model complexity to penalize overparameterization systematically; Practice modern time series validation techniques, such as rolling-origin cross-validation, to ensure robust out-of-sample performance. You will start by exploring core definitions and statistical assumptions before moving on to step-by-step calculations and comparative analysis of model selection criteria. This course is designed for aspiring data analysts, programmers, and forecasting enthusiasts who want a solid foundation in model evaluation; no advanced mathematical background is required. Start reading today to confidently select and validate your time series models.