You are building a forecasting model for a digital content platform that needs to predict daily demand for creative assets so ranking, caching, and promotion decisions can be made ahead of time. The core challenge is that the signal is temporal, with seasonality, trends, and recent activity all affecting future demand.
How would you approach feature engineering for this time series problem so the model captures temporal patterns without leaking future information, and how would you train and evaluate the resulting forecasting system?