Intelligent forecasting with limited data availability utilizing embeddings from auto-encoders and machine learning models

A method comprising:

• receiving activity data for a user, wherein the activity data comprises historical activities by the user over a time period;
• extracting model feature data for a plurality of model features associated with a machine learning (ML) model from the activity data;
• generating a plurality of embeddings for the plurality of model features from the activity data, wherein the plurality of embeddings are each associated with individual activities from the historical activities by the user over the time period;
• applying an attention layer to the plurality of embeddings, wherein the attention layer applies weights on particular features from the plurality of model features in the plurality of embeddings;
• generating, using a long-short term memory (LSTM) model, a vector from the plurality of embeddings, wherein the generating includes utilizing an ML layer for output of the vector;
• providing the vector to the ML model, wherein the ML model is trained using a plurality of other past activity vectors associated with additional historical activities by a plurality of other users;
• and
• determining, using the ML model based on the providing, a risk score of the user for failing to meet a required stipulation of a service extended to the user at a future time.

A non-transitory machine-readable medium having stored thereon machine-readable instructions executable to cause a machine to perform operations comprising:

• receiving data associated with past activities of a user;
• generating, using an encoding operation of a machine learning (ML) framework, a plurality of embeddings for activity features of the past activities based on the data;
• generating, using an ML layer of the ML framework, a vector encoded from the plurality of embeddings;
• and
• forecasting, using the ML framework, a likelihood of a user action by the user at a future time based on the vector, wherein the forecasting is performed using an ML model trained using a plurality of other vectors generated using additional past activities of a plurality of other users.