Abstract: Methods, systems, and computer storage media for providing a dynamically weighted unobserved component model (“DW-UCM”) in a demand forecasting engine of a data analytics system. Dynamic weighting is performed based on a machine learning framework that includes tools, interfaces, and a library for developing improved machine learning models (e.g., dynamic demand forecasting models) of a dynamic weighting machine learning pipeline. In particular, the dynamic weighting machine learning pipeline can include a first module that is configured to predict if a segment (e.g., travel segment) under evaluation is open or closed (e.g., due to a restriction or rule), a second module that forecasts near-term recovery (e.g., approx. 0 - 4 weeks), and a third module that predicts longer term recovery.