Abstract: Requests for computing resources and other resources can be predicted and managed. For example, a system can determine a baseline prediction indicating a number of requests for an object over a future time-period. The system can then execute a first model to generate a first set of values based on seasonality in the baseline prediction, a second model to generate a second set of values based on short-term trends in the baseline prediction, and a third model to generate a third set of values based on the baseline prediction. The system can select a most accurate model from among the three models and generate an output prediction by applying the set of values output by the most accurate model to the baseline prediction. Based on the output prediction, the system can cause an adjustment to be made to a provisioning process for the object.

Abstract: Requests for computing resources and other resources can be predicted and managed. For example, a system can determine a baseline prediction indicating a number of requests for an object over a future time-period. The system can then execute a first model to generate a first set of values based on seasonality in the baseline prediction, a second model to generate a second set of values based on short-term trends in the baseline prediction, and a third model to generate a third set of values based on the baseline prediction. The system can select a most accurate model from among the three models and generate an output prediction by applying the set of values output by the most accurate model to the baseline prediction. Based on the output prediction, the system can cause an adjustment to be made to a provisioning process for the object.

Abstract: One exemplary system can receive a selection of a dataset via a graphical user interface (GUI). The dataset can represent a time-series projection. The system can feed the dataset into a first machine-learning model to obtain an output indicating whether the time-series projection has a data value that should be overridden with an override value. If the first machine-learning model indicates that the time-series projection has the data value that should be overridden, the system can feed the data value as input to a second machine-learning model to obtain an output indicating whether the override value should be greater than or less than the data value. The system can then render a visual directionality cue within the GUI based on the output from the second machine-learning model. The visual directionality cue can provide guidance for overriding the data value.