Self-Adaptive Forecasting for Improved Deep Learning on Non-Stationary Time-Series

Understand

Real-world time-series datasets often violate the assumptions of standard supervised learning for forecasting -- their distributions evolve over time, rendering the conventional training and model selection procedures suboptimal.

• In this paper, we propose a novel method, Self-Adaptive Forecasting (SAF), to modify the training of time-series forecasting models to improve their performance on forecasting tasks with such non-stationary time-series data.
• SAF integrates a self-adaptation stage prior to forecasting based on `backcasting', i.e.
• predicting masked inputs backward in time.