User behavior-based machine learning in entity account configuration

A non-transitory computer-readable storage medium containing computer program code that, when executed by a hardware processor, causes the hardware processor to perform steps comprising:

• training, by an online system, a machine-learned model by:
• initializing weights of the machine-learned model with an initial set of values to produce an initialized machine-learned model;
• accessing historical seasonality data from a plurality of time intervals;
• generating a plurality of training vectors based on the historical seasonality data, wherein each of the plurality of training vectors is associated with a time interval of the plurality of time intervals, and wherein each respective training vector is associated with a label indicating a usage amount of a respective time interval;
• for each of the plurality of training vectors, applying the initialized machine-learned model to the training vector to generate a prediction of a funding amount for the respective time interval based on the respective training vector;
• and
• updating the weights of the initialized machine-learned model based on 1) the predictions and the label associated with each of the training vectors until a difference between the predictions and the label associated with each of the training vectors is within a threshold difference and 2) a predicted usage associated with each of the plurality of time intervals and an actual usage associated with each of the plurality of time intervals to produce the machine-learned model.

A system comprising:

• a hardware processor;
• and
• a non-transitory computer-readable medium containing instructions that, when executed by the hardware processor, cause the hardware processor to train a machine-learned model by:
• initializing weights of the machine-learned model with an initial set of values to produce an initialized machine-learned model;
• accessing historical seasonality data from a plurality of time intervals;
• generating a plurality of training vectors based on the historical seasonality data, wherein each of the plurality of training vectors is associated with a time interval of the plurality of time intervals, and wherein each respective training vector is associated with a label indicating a usage amount of a respective time interval;
• for each of the plurality of training vectors, applying the initialized machine-learned model to the training vector to generate a prediction of a funding amount for the respective time interval based on the respective training vector;
• and
• updating the weights of the initialized machine-learned model based on 1) the predictions and the label associated with each of the training vectors until a difference between the predictions and the label associated with each of the training vectors is within a threshold difference and 2) a predicted usage associated with each of the plurality of time intervals and an actual usage associated with each of the plurality of time intervals to produce the machine-learned model.