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Pattern Identification in Time-Series Social Media Data, and Output-Dynamics Engineering for a Dynamic System Having One or More Multi-Scale Time-Series Data Sets
Pattern Identification in Time-Series Social Media Data, and Output-Dynamics Engineering for a Dynamic System Having One or More Multi-Scale Time-Series Data Sets
In some aspects, computer-implemented methods of identifying patterns in time-series social-media data. In an embodiment, the method includes applying a deep-learning methodology to the time-series social-media data at a plurality of temporal resolutions to identify patterns that may exist at and across ones of the temporal resolutions. A particular deep-learning methodology that can be used is a recursive convolutional Bayesian model (RCBM) utilizing a special convolutional operator. In some aspects, computer-implemented methods of engineering outcome-dynamics of a dynamic system. In an embodiment, the method includes training a generative model using one or more sets of time-series data and solving an optimization problem composed of a likelihood function of the generative model and a score function reflecting a utility of the dynamic system. A result of the solution is an influence indicator corresponding to intervention dynamics that can be applied to the dynamic system to influence outcome dynamics of the system.
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