Systems and methods for validating forecasting machine learning models

A device, comprising:

• one or more processors configured to:
• receive historical time series data and output data associated with a forecasting model, wherein the historical time series data includes univariate time series data;
• train a proxy regression model with the historical time series data and the output data, wherein the trained proxy regression model outputs inference data identifying an importance of each time step of the historical time series data;
• analyze the historical time series data, the output data, and the inference data to identify confidential information in the historical time series data, the output data, and the inference data;
• create perturbed data from the historical time series data, the output data, and the inference data, wherein the perturbed data comprises noisy or synthetic data that protects the confidential information in the historical time series data, the output data, and the inference data;
• process the perturbed data, with the trained proxy regression model, to generate labelled data and to identify top features of the labelled data relative to other features of the labelled data;
• process subsets of the top features of the labelled data, with the trained proxy regression model, to determine feature data identifying an importance of each of the subsets of the top features of the labelled data;
• determine, based on the feature data, a plurality of types of error values associated with the trained proxy regression model;
• calculate validation data for validating the forecasting model based on the plurality of types of error values associated with the trained proxy regression model;
• validate the forecasting model with the validation data;
• and
• retrain the trained proxy regression model with the validation data.

A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising:

• one or more instructions that, when executed by one or more processors of a device, cause the device to:
• receive historical time series data and output data associated with a forecasting model;
• train a proxy regression model with the historical time series data and the output data, wherein the trained proxy regression model outputs inference data identifying an importance of each time step of the historical time series data;
• analyze the historical time series data, the output data, and the inference data to identify confidential information in the historical time series data, the output data, and the inference data;
• create perturbed data from the historical time series data, the output data, and the inference data, wherein the perturbed data comprises noisy or synthetic data that protects the confidential information in the historical time series data, the output data, and the inference data;
• process the perturbed data, with the trained proxy regression model, to generate labelled data and to identify top features of the labelled data relative to other features of the labelled data;
• process, by the device, subsets of the top features of the labelled data, with the trained proxy regression model, to determine feature data identifying an importance of each of the subsets of the top features of the labelled data;
• determine, by the device and based on the feature data, a plurality of types of error values associated with the trained proxy regression model;
• calculate validation data for validating the forecasting model based on the plurality of types of error values associated with the trained proxy regression model;
• validate the forecasting model with the validation data;
• and
• retrain the trained proxy regression model with the validation data.