Double descent for time series forecasting

Abstract

A 'modern regime' training framework for deep learning-based time series forecasting models is described herein. In summary, the “modern regime” training framework consists of configuring the hyperparameters of the training process in a manner that substantially increases the complexity of the time series forecasting model beyond conventional norms and enables the time series forecasting model to achieve a “double descent” during the training process. In essence, a “double descent” has occurred when, after initially improving and reaching a local maximum performance, the performance of the time series forecasting model is allowed to deteriorate substantially and then improve again to reach a performance that exceeds the initial local maximum performance.