Geometric aging data reduction for machine learning applications

One or more non-transitory computer-readable media storing instructions, which, when executed by one or more hardware processors, cause:

• receiving a first time-series dataset that tracks at least one metric value over time;
• generating a second time-series dataset that includes a reduced version of a first portion of the first time-series dataset and a non-reduced version of a second portion of the first time-series dataset, wherein the second portion of the first time-series dataset includes metric values that are more recent than the first portion of the first time-series dataset;
• determining one or more virtual metrics for the first portion of the first time-series dataset based on a non-reduced version of the first portion of the first time-series dataset, wherein the one or more virtual metrics are not discernable from the reduced version of the first portion of the first time-series dataset;
• and
• training at least one machine learning model using the second time-series dataset that includes the reduced version of the first portion of the first time-series dataset and the non-reduced version of the second portion of the first time-series dataset.

A system comprising:

• one or more hardware processors;
• one or more non-transitory computer-readable media storing instructions, which, when executed by one or more hardware processors, cause performance of operations comprising:
• receiving a first time-series dataset that tracks at least one metric value over time;
• generating a second time-series dataset that includes a reduced version of a first portion of the first time-series dataset and a non-reduced version of a second portion of the first time-series dataset, wherein the second portion of the first time-series dataset includes metric values that are more recent than the first portion of the first time-series dataset;
• determining one or more virtual metrics for the first portion of the first time-series dataset based on a non-reduced version of the first portion of the first time-series dataset, wherein the one or more virtual metrics are not discernable from the reduced version of the first portion of the first time-series dataset;
• and
• training at least one machine learning model using the second time-series dataset that includes the reduced version of the first portion of the first time-series dataset and the non-reduced version of the second portion of the first time-series dataset.