Abstract: Techniques for geometric aging data reduction for machine learning applications are disclosed. In some embodiments, an artificial-intelligence powered system receives a first time-series dataset that tracks at least one metric value over time. The system then generates a second time-series dataset that includes a reduced version of a first portion of the time-series dataset and a non-reduced version of a second portion of the time-series dataset. The second portion of the time-series dataset may include metric values that are more recent than the first portion of the time-series dataset. The system further trains a machine learning model using the second time-series dataset that includes the reduced version of the first portion of the time-series dataset and the non-reduced version of the second portion of the time-series dataset. The trained model may be applied to reduced and/or non-reduced data to detect multivariate anomalies and/or provide other analytic insights.

Abstract: Techniques for geometric aging data reduction for machine learning applications are disclosed. In some embodiments, an artificial-intelligence powered system receives a first time-series dataset that tracks at least one metric value over time. The system then generates a second time-series dataset that includes a reduced version of a first portion of the time-series dataset and a non-reduced version of a second portion of the time-series dataset. The second portion of the time-series dataset may include metric values that are more recent than the first portion of the time-series dataset. The system further trains a machine learning model using the second time-series dataset that includes the reduced version of the first portion of the time-series dataset and the non-reduced version of the second portion of the time-series dataset. The trained model may be applied to reduced and/or non-reduced data to detect multivariate anomalies and/or provide other analytic insights.

Abstract: Techniques for generating imputation-based, uniformly sampled parallel streams of time-series data are disclosed. A system divides into two subsets a dataset made up of multiple data streams. The data streams include interpolated data. The system trains one data correlation model using one subset of the data and applies the trained model to the other subset. The system replaces the interpolated values in the other subset with estimated values generated by the model. The system trains another data correlation model using the revised subset. The system applies the new model to the initial subset to generate estimated values for the initial subset. The system replaces the interpolated values in the initial subset with the estimated values. The system repeats the process of training data correlation models and revising previously-interpolated data points in the subsets of data until a predetermined iteration threshold is met.