Abstract: Systems and methods for automatic forecasting are described. Embodiments of the present disclosure receive a time-series dataset; compute a time-series meta-feature vector based on the time-series dataset; generate a performance score for a forecasting model using a meta-learner machine learning model that takes the time-series meta-feature vector as input; select the forecasting model from a plurality of forecasting models based on the performance score; and generate predicted time-series data based on the time-series dataset using the selected forecasting model.

Abstract: Certain embodiments involve tracking incremental updates to graph data structures and thereby facilitating efficient data retrieval. For instance, a computing system services a first query for one or more segments of computing devices, online entities, or both. The computing system services the first query by searching of a set of nodes from a graph data structure. The computing system receives a second query after the graph data structure has been modified. The computing system identifies, from a change list for tracking changes to the graph data structure, a subset of the nodes impacted by the modification to the graph data structure. The computing system services the second query by searching the subset of impacted nodes in the graph data structure.

Abstract: The disclosure describes one or more implementations of a serverless computing management system that utilizes an online learning model to dynamically adjust the number of serverless execution containers in a serverless pool based on incoming data patterns. For example, for each time instance in a given time period, the serverless computing management system utilizes the online learning model to balance computing latency and computing cost to determine how to intelligently resize the serverless pool, such that the online machine-learning models in the serverless pool can update in a manner that improves accuracy and computing efficiency while also minimizing unnecessary delays. Further, the serverless computing management system provides a framework that facilitates state-based training of online machine-learning models in a stateless and serverless cloud-based environment.

Abstract: Certain embodiments involve tracking incremental updates to graph data structures and thereby facilitating efficient data retrieval. For instance, a computing system services a first query for one or more segments of computing devices, online entities, or both. The computing system services the first query by searching of a set of nodes from a graph data structure. The computing system receives a second query after the graph data structure has been modified. The computing system identifies, from a change list for tracking changes to the graph data structure, a subset of the nodes impacted by the modification to the graph data structure. The computing system services the second query by searching the subset of impacted nodes in the graph data structure.