Double descent for time series forecasting

A non-transitory computer-readable medium that stores program instructions for training a deep learning-based time series forecasting model that, when executed by a processor, cause the processor to:

• receive (i) a training dataset including a first plurality of time series data and (ii) a testing dataset including a second plurality of time series data;
• train the time series forecasting model using the training dataset, the time series forecasting model having a plurality of parameters that are learned during the training;
• and
• periodically during the training, evaluate a performance of the time series forecasting model against the testing dataset,
• wherein the training proceeds for a sufficient number of training epochs that, after initially improving and reaching a local maximum performance, the performance of the time series forecasting model is allowed to deteriorate and then improve again to reach a performance that exceeds the local maximum performance.

A system for training a deep learning-based time series forecasting model, the system comprising:

• a memory configured to store (i) a training dataset including a first plurality of time series data and (ii) a testing dataset including a second plurality of time series data;
• and
• a processor configured to:
• train the time series forecasting model using the training dataset, the time series forecasting model having a plurality of parameters that are learned during the training;
• and
• periodically during the training, evaluate a performance of the time series forecasting model against the testing dataset,
• wherein the training proceeds for a sufficient number of training epochs that, after initially improving and reaching a local maximum performance, the performance of the time series forecasting model is allowed to deteriorate and then improve again to reach a performance that exceeds the local maximum performance.