Prediction model training using detected anomalies

A method for a prediction model, comprising:

• receiving historical data;
• and
• determining, using a processor, a training data set and a test data set from the historical data;
• training a plurality of models using the training data set to obtain a plurality of trained models;
• determining a best trained model of the plurality of trained models using the test data set;
• selecting hyperparameters associated with the best trained model;
• generating a prediction model using the hyperparameters and the historical data to obtain a trained prediction model;
• determining at least one detected anomaly based on a difference between a forecast and the output of the trained prediction model;
• providing the forecast, the output of the trained model, and the at least one detected anomaly to a user using a user feedback interface;
• receiving user feedback from the user using the user feedback interface, wherein the user feedback comprises a false detected anomaly indication indicating that the at least one anomaly is not an anomaly;
• and
• retraining the trained prediction model using the hyperparameters and the user feedback to obtain a retrained prediction model.

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

• receiving historical data;
• and
• determining, using a processor, a training data set and a test data set from the historical data;
• training a plurality of models using the training data set to obtain a plurality of trained models;
• determining a best trained model of the plurality of trained models using the test data set;
• selecting hyperparameters associated with the best trained model;
• generating a prediction model using the hyperparameters and the historical data to obtain a trained prediction model;
• determining at least one detected anomaly based on a difference between a forecast and the output of the trained prediction model;
• providing the forecast, the output of the trained model, and the at least one detected anomaly to a user using a user feedback interface;
• receiving user feedback from the user using the user feedback interface, wherein the user feedback comprises a false detected anomaly indication indicating that the at least one anomaly is not an anomaly;
• and
• retraining the trained prediction model using the hyperparameters and the user feedback to obtain a retrained prediction model.