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

A method for performing time series prediction, the method being implemented by at least one processor, the method comprising:

• receiving, by the at least one processor, first information that relates to a source distribution of a time series;
• receiving, by the at least one processor, second information that relates to a target distribution of the time series;
• extracting, by the at least one processor, at least one latent code from a plurality of samples of the target distribution;
• perturbing, by the at least one processor, the extracted at least one latent code by adding a predetermined amount of random noise in order to generate at least one augmented sample of the target distribution;
• training, by the at least one processor, a classifier based on a plurality of samples of the source distribution;
• adjusting, by the at least one processor, the classifier based on a combination of the plurality of samples of the source distribution and a predetermined number of the at least one augmented sample of the target distribution;
• and
• training, by the at least one processor by using the adjusted classifier, a predetermined machine learning model that is usable for making future predictions that relate to the time series,
• wherein the time series comprises a multivariate time series, and
• wherein the multivariate time series comprises one from among a second time series that relates to yield rate curve data, a third time series that relates to weather forecasting data, and a fourth time series that relates to medical diagnosis data.

A computing apparatus for performing time series prediction, the computing apparatus comprising:

• a processor;
• a memory;
• and
• a communication interface coupled to each of the processor and the memory,
• wherein the processor is configured to:
• receive, via the communication interface, first information that relates to a source distribution of a time series;
• receive, via the communication interface, second information that relates to a target distribution of the time series;
• extract at least one latent code from a plurality of samples of the target distribution;
• perturb the extracted at least one latent code by adding a predetermined amount of random noise in order to generate at least one augmented sample of the target distribution;
• train a classifier based on a plurality of samples of the source distribution;
• adjust the classifier based on a combination of the plurality of samples of the source distribution and a predetermined number of the at least one augmented sample of the target distribution;
• and
• train, by using the adjusted classifier, a predetermined machine learning model that is usable for making future predictions that relate to the time series,
• wherein the time series comprises a univariate time series, and
• wherein the univariate time series comprises a first time series that relates to stock market data.

A computing apparatus for performing time series prediction, the computing apparatus comprising:

• a processor;
• a memory;
• and
• a communication interface coupled to each of the processor and the memory,
• wherein the processor is configured to:
• receive, via the communication interface, first information that relates to a source distribution of a time series;
• receive, via the communication interface, second information that relates to a target distribution of the time series;
• extract at least one latent code from a plurality of samples of the target distribution;
• perturb the extracted at least one latent code by adding a predetermined amount of random noise in order to generate at least one augmented sample of the target distribution;
• train a classifier based on a plurality of samples of the source distribution;
• adjust the classifier based on a combination of the plurality of samples of the source distribution and a predetermined number of the at least one augmented sample of the target distribution;
• and
• train, by using the adjusted classifier, a predetermined machine learning model that is usable for making future predictions that relate to the time series,
• wherein the time series comprises a multivariate time series, and
• wherein the multivariate time series comprises one from among a second time series that relates to yield rate curve data, a third time series that relates to weather forecasting data, and a fourth time series that relates to medical diagnosis data.