Method and system for learnable augmentation for time series prediction under distribution shifts

Abstract

A method and a system for using a learnable augmentation technique to perform few-shot calibration of a model that is designed to generate time series predictions under distribution shifts with improved accuracy are provided. The method includes: receiving first information that relates to a source distribution of a time series and second information that relates to a target distribution of the time series; extracting a latent code from samples of the target distribution; perturbing the latent code by adding random noise in order to generate augmented samples of the target distribution; training a classifier based on samples of the source distribution; adjusting the classifier based on a combination of the samples of the source distribution and the augmented samples of the target distribution; and using the adjusted classifier to train a machine learning model that is usable for making future predictions that relate to the time series.