In machine learning, learning curves are a metric that plots performance versus training set size. They inform decisions about data acquisition, model selection, and hyperparameter tuning. Despite their importance, recent research suggests that our understanding of learning curve behavior remains limited. In this work, we investigate learning curves from a classification perspective to better understand their structural properties. By framing learning curves as time series and applying time series classification (TSC) techniques, we uncover several key findings: (1) training accuracy curves are significantly more distinguishable across models than validation or test curves; (2) learning curves become more informative and discriminative after a sufficient number of anchor points; and (3) TSC models that emphasize global structural features outperform those focused on local or pointwise characteristics. These results not only offer new insights into the nature of learning curves but also suggest promising directions for future work, including the development of specialized models that move beyond conventional time series assumptions.