Predicting the traffic of an article, as measured by page views, is of great importance to content providers. Articles with increased traffic can improve advertising revenue and expand a provider’s user base. We propose a broadly applicable methodology incorporating meta-data and joint forecasting across articles, that involves solving a large optimization problem through the Alternating Directions Method of Multipliers (ADMM). We implement our solution using Spark, and evaluate it over a large corpus of articles and forecasting models. Our results demonstrate that our feature-based forecasting is both scalable as well as highly accurate, significantly improving forecasting predictions compared to traditional forecasting models.