Self-Adaptive Forecasting for Improved Deep Learning on Non-Stationary Time-Series. (arXiv:2202.02403v2 [cs.LG] UPDATED)

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. This is a …

arxiv deep learning forecasting learning lg series time