This chapter introduces adaptive learning forecasting, a novel approach that enhances forecasting accuracy by incorporating both forecast error learning and forecast averaging. The univariate and multivariate versions of adaptive learning forecasting are thoroughly explained. It presents the theoretical framework along with the necessary mathematical derivations that establish the desirable properties and practical usefulness of the method. In particular, the chapter demonstrates how ex ante MSE-based improvements are available from the use of the method. Additionally, it offers a step-by-step guide on implementing adaptive learning techniques in real-world applications. Using agricultural price data, the chapter illustrates the methodology and highlights the advantages of multivariate adaptive learning over its univariate counterpart, making the method a particularly appealing part of the forecaster’s toolbox.