Abstract: A computerized system and method for allocating multi-functional or multi-feature resources (which may handle multiple functions or tasks, e.g., simultaneously) for a plurality of time intervals, including: transforming an initial allocation matrix (which may associate each resource with a single function, task, or feature - and may not address simultaneous handling of tasks or task types by the resources) into an updated allocation matrix, where the updated allocation matrix includes a plurality of feature matrices describing different multi-feature resources to be allocated; predicting, using a machine learning (ML) model, expected service metrics for the updated allocation matrix; and providing a final allocation matrix based on the expected service metrics. Embodiments may perform iterative calculations and/or transformations of data to improve allocation matrices and provide a final allocation matrix for which predicted service metrics correspond to required or optimal service metrics.

Abstract: Methods and systems for, upon receipt of a second computer data stream, predicting a change in processing a first computer data stream, include: receiving, at a computing device, the first computer data stream; generating a first data sequence comprising a time of receipt of the first computer data stream; receiving the second computer data stream; generating a second data sequence comprising a time of receipt of the second computer data stream; sending the first and second data sequences to a prediction model; predicting, by the prediction model, at least one change in at least one metric associated with processing the first computer data stream, the predicted change based at least in part on the first and second data sequences; and sending, by the prediction model, to the computing device, the at least one change in the at least one metric associated with processing the first computer data stream.