Prediction model training using detected anomalies
A system for a prediction model includes an interface and a processor. The interface is configured to receive historical data. The processor is configured to determine hyperparameters based at least in part on a best model of N models; determine the prediction model by training using the hyperparameters on the historical data; determine detected anomalies based at least in part on an output of the prediction model; receive user feedback on the detected anomalies and undetected anomalies; and retrain the prediction model using the hyperparameters and based on the user feedback.
1 . A system for a prediction model, comprising:
an interface configured to:
receive historical data; and
a processor configured to:
determine a training data set and a test data set from the historical data;
train a plurality of models using the training data set to obtain a plurality of trained models;
determine a best trained model of the plurality of trained models using the test data set;
select hyperparameters associated with the best trained model;
generate a prediction model using the hyperparameters and the historical data to obtain a trained prediction model;
receive a forecast;
determine an output of the trained prediction model corresponding to the forecast;
determine at least one detected anomaly based on a difference between the forecast and the output of the trained prediction model exceeding a threshold;
provide the forecast, the output of the trained model, and the at least one detected anomaly to a user using a user feedback interface;
receive 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
an undetected anomaly indication indicating that an undetected anomaly is an anomaly; and
retrain the trained prediction model using the hyperparameters and the user feedback to obtain a retrained prediction model.
2 . The system of claim 1 , wherein the historical data is preprocessed.
3 . The system of claim 2 , wherein preprocessing comprises normalizing the historical data.
4 . The system of claim 2 , wherein preprocessing comprises differencing the historical data.
5 . The system of claim 1 , wherein the training data set comprises a first portion of the historical data from an earliest time period of the historical data.
6 . The system of claim 1 , wherein the training data set comprises a first portion of the historical data from a first time period and the testing data set comprises a second portion of the historical data from a second time period, wherein the second time period is a more recent time period than the first time period.
7 . The system of claim 1 , wherein the output of the trained prediction model is postprocessed.
8 . The system of claim 7 , wherein post-processing comprises inverse differencing the output of the trained prediction model.
9 . The system of claim 7 , wherein post-processing comprises de-normalizing the output of the trained prediction model.
10 . The system of claim 1 , wherein the forecast is received using the interface.
11 . The system of claim 1 , wherein the processor is further configured to determine to get the user feedback.
12 . A method for a prediction model, comprising:
receiving historical data using a user interface;
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;
determine 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;
receiving a forecast;
determining an output of the trained prediction model corresponding to the forecast;
determining at least one detected anomaly based on a difference between the forecast and the output of the trained prediction model exceeding a threshold;
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
an undetected anomaly indication indicating that an undetected anomaly is an anomaly; and
retraining the trained prediction model using the hyperparameters and the user feedback to obtain a retrained prediction model.
13 . 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 using a user interface;
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;
determine 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;
receiving a forecast;
determining an output of the trained prediction model corresponding to the forecast;
determining at least one detected anomaly based on a difference between the forecast and the output of the trained prediction model exceeding a threshold;
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
an undetected anomaly indication indicating that an undetected anomaly is an anomaly; and
retraining the trained prediction model using the hyperparameters and the user feedback to obtain a retrained prediction model.