Time-based ensemble machine learning model

A method, comprising:

• sorting an input dataset into a first version of data and a second version of data, wherein the first version of data is associated with a first period of time and the second version of data is associated with a second period of time, wherein the second period of time is a shorter period of time than the first period of time;
• generating a first set of one or more machine learning models based on the first version of data;
• generating a second set of one or more machine learning models based on the second version of data;
• combining the first set of one or more machine learning models and the second set of one or more machine learning models to generate a combined ensemble model;
• and
• output a prediction based on the combined ensemble model, wherein the prediction indicates abnormal behavior associated with the input dataset.

A computer program product, the computer program product being embodied in a non-transitory computer readable storage medium and comprising computer instructions for:

• sorting an input dataset into a first version of data and a second version of data, wherein the first version of data is associated with a first period of time and the second version of data is associated with a second period of time, wherein the second period of time is a shorter period of time than the first period of time;
• generating a first set of one or more machine learning models based on the first version of data;
• generating a second set of one or more machine learning models based on the second version of data;
• combining the first set of one or more machine learning models and the second set of one or more machine learning models to generate an ensemble model;
• and
• output a prediction based on the ensemble model, wherein the prediction indicates indicate abnormal behavior associated with the input dataset.