Abstract: Methods, systems, and computer storage media for providing a data analytics index associated with a crisis-recovery data analytics engine in a data analytics system. The data analytics index is a consolidated single index representation of a set of variables associated with a set of consumer behaviors. The crisis-recovery data analytics engine supports generating the data analytics index associated with a pre-crisis period and a crisis-recovery period. In operation, a crisis-recovery dataset—associated with a set of variables of a set of consumer behaviors that support quantifying recovery from a crisis event—is accessed. The set of consumer behaviors are selected based on a crisis-recovery machine learning model that is trained on a pre-crisis dataset and a crisis dataset for selecting the set of consumer behaviors. A data analytics index is generated based on the crisis-recovery dataset. A data visualization comprising crisis-recovery data indicating recovery from the crisis event is generated.

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.

Abstract: Methods, systems, and computer storage media for providing a unified multilayer-based index for a contextual geoanalytics engine in a data analytics system. The contextual geoanalytics engine is configured to aggregate point-of-interest geographical data from multiple data sources into an aggregate or composite dataset. The contextual geoanalytics engine then transforms and maps the data into a homogenous dataset—i.e., a location embedding record that is homogenous representation of an aggregated dataset—comparable across global geographical regions. The homogenous dataset is accessible via the unified multilayer-based index that is a single geographical index, where the homogenous dataset is a composite of different datasets. The data includes different data types, where the data types are stored in different layers while sharing a common index (i.e., unified multilayer-based index).