US 12,332,917 B2
Univariate time series segmentation using proxy variables and sparse graph recovery algorithms
Harsh Shrivastava, Redmond, WA (US); and Shima Imani, Sammamish, WA (US)
Assigned to Microsoft Technology Licensing, LLC, Redmond, WA (US)
Filed by Microsoft Technology Licensing, LLC, Redmond, WA (US)
Filed on Mar. 24, 2023, as Appl. No. 18/189,933.
Prior Publication US 2024/0411779 A1, Dec. 12, 2024
Int. Cl. G06F 16/28 (2019.01); G06F 16/26 (2019.01); G06F 17/18 (2006.01); G06F 18/2323 (2023.01)
CPC G06F 16/285 (2019.01) [G06F 16/26 (2019.01); G06F 18/2323 (2023.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method for segmenting univariate time series data comprising:
generating multiple proxy variable time series for a univariate time series, wherein a first proxy variable time series of the multiple proxy variable time series is based on the univariate time series;
generating a supplemented multivariate time series by supplementing the univariate time series with the multiple proxy variable time series;
grouping portions of the supplemented multivariate time series by a window size to generate windowed subsequences of the supplemented multivariate time series;
generating graph objects from the windowed subsequences utilizing a sparse graph recovery model, wherein the graph objects indicate correlation values between nodes; and
determining one or more segmentation timestamps indicating one or more segment changes in the supplemented multivariate time series utilizing a conditional similarity model conditioned on the univariate time series that determines when changes between correlation values in graph objects meet or exceed a difference threshold.