Abstract: One example method includes using an ensemble including machine learning (ML) forecasting models, determining respective relationships between past media spends and changes in search engine optimization (SEO) driven consumer demand for a product or service, ranking the relationships according to a criterion, based on the ranking, generating a forecast that comprises recommended future media spends, and effects expected to be achieved by those future media spends, and implementing the recommended future media spends.

Abstract: Methods, apparatus, and processor-readable storage media for resource prioritization using machine learning techniques are provided herein. An example computer-implemented method includes obtaining data pertaining to multiple resources associated with at least one enterprise; prioritizing one or more of the multiple resources in connection with one or more tasks associated with the at least one enterprise by processing, using one or more machine learning techniques, at least a portion of the data pertaining to the multiple resources and data pertaining to the one or more tasks; and performing one or more automated actions based at least in part on the prioritizing of the one or more resources.

Abstract: A method for generating final prediction data, that includes generating, by a prediction generator, validation prediction data using a prediction model, making a first determination that the validation prediction data includes unexplained variance data, in response to making the first determination, isolating unexplained variance data from the validation prediction data, generating initial prediction data using the prediction model, isolating prediction trend data from the initial prediction data, obtaining the final prediction data by summing the prediction trend data with the unexplained variance data, receiving, from a user, a constraint, making a second determination that the final prediction data satisfies the constraint, and based on the second determination, indicating, to the user, that the final prediction data satisfies the constraint.