Systems and methods for time-series classification through residual learning

A system for training a time series classification model, the system comprising:

• a processor;
• and
• memory containing instructions that, when executed by the processor, cause the processor to perform the following:
• transforming time-series ground-truth data into continuous data;
• training a time-series forecasting model to predict future values based on the transformed continuous data, yielding a forecast output;
• projecting the forecast output into a two-dimensional representation of estimated continuous data;
• training a residual model to determine residuals between the continuous data and the estimated continuous data;
• integrating the determined residuals into embeddings of the time-series forecasting model;
• retraining the time-series forecasting model with the embedded residuals to minimize cross-entropy loss associated with the time-series forecasting model;
• and
• upon convergence of the cross-entropy loss, outputting a trained time-series forecasting model configured to predict classifications for time-series data.

A non-transitory computer readable medium storing instructions that, when executed by a processor, cause the processor to perform:

• transforming time-series ground-truth data into continuous data;
• training a time-series forecasting model to predict future values based on the transformed continuous data, yielding a forecast output;
• projecting the forecast output into a two-dimensional representation of estimated continuous data;
• training a residual model to determine residuals between the continuous data and the estimated continuous data;
• integrating the determined residuals into embeddings of the time-series forecasting model;
• retraining the time-series forecasting model with the embedded residuals to minimize cross-entropy loss associated with the time-series forecasting model;
• and
• upon convergence of the cross-entropy loss, outputting a trained time-series forecasting model configured to predict classifications for time-series data.