Time-factored performance prediction

A computing device for predicting performance of a target entity, the computing device comprising:

• a time-factored aggregator configured to allocate training query intents generated from a query-URL click graph for multiple training entities into training time intervals in a time series based on a corresponding query intent time for each training query intent, the training time intervals in the time series being separated by training milestones in the time series, and to allocate training performance results for the multiple training entities into the training time intervals in the time series based on a corresponding performance time of each training performance result;
• and
• a machine learning model configured to receive the allocated training query intents and allocated training performance results, the machine learning model being trained for one or more of the training milestones of the time series based on the training query intents allocated to a training time interval prior to the training milestone and the training performance results allocated to a training time interval after the training milestone, the machine learning model being further configured to generate a prediction of a target performance result for the target entity for an interval after a target milestone in the time series by inputting to the trained machine learning model target query intents allocated to the target entity in a target time interval before the target milestone.

One or more tangible processor-readable storage media of a tangible article of manufacture encoding processor-executable instructions for executing on an electronic computing system a process of predicting performance of a target entity using a machine learning model, the process comprising:

• allocating training query intents generated from a query-URL click graph for multiple training entities into training time intervals in a time series based on a corresponding query intent time for each training query intent, the training time intervals in the time series being separated by training milestones in the time series;
• allocating training performance results for the multiple training entities into the training time intervals in the time series based on a corresponding performance time of each training performance result;
• training the machine learning model for a training milestone of the time series based on the training query intents allocated to a training time interval prior to the training milestone and the training performance results allocated to a training time interval after the training milestone;
• allocating target query intents generated from the query-URL click graph for the target entity into target time intervals based on a corresponding query intent time for each target query intent, the target time intervals in the time series being separated by target milestones in the time series;
• and
• generating a prediction of a target performance result for the target entity for an interval after a target milestone in the time series by inputting to the trained machine learning model target query intents allocated to the target entity in a target time interval before the target milestone.