This talk gives an overview of applying HDC for feature encoding prior to aggregation. This approach can be useful in several domains, including image processing for spatial features, time series for temporal sequences, and any other distinct features. Typically represented as vectors, features are commonly combined through superposition to create compact representations for tasks like classification. HDC, equipped with operators like superposition and binding, provides a practical way to incorporate more contextual or temporal knowledge into these vector-based representations. For instance, the presentation shows how HDC temporal encoding can enhance time series classification algorithms across different fields such as automotive or biomedical signals.