US 11,768,912 B2
Performing multivariate time series prediction with three-dimensional transformations
Mu Qiao, Belmont, CA (US); Yuya Jeremy Ong, Tenafly, NJ (US); and Divyesh Jadav, San Jose, CA (US)
Assigned to International Business Machines Corporation, Armonk, NY (US)
Filed by International Business Machines Corporation, Armonk, NY (US)
Filed on Jul. 12, 2019, as Appl. No. 16/510,712.
Prior Publication US 2021/0012191 A1, Jan. 14, 2021
Int. Cl. G06F 17/18 (2006.01); G06N 3/08 (2023.01); G06F 17/15 (2006.01); G06N 20/10 (2019.01); G06F 18/213 (2023.01)
CPC G06F 17/18 (2013.01) [G06F 17/15 (2013.01); G06F 18/213 (2023.01); G06N 3/08 (2013.01); G06N 20/10 (2019.01)] 19 Claims
OG exemplary drawing
 
1. A computer-implemented method, comprising:
receiving historical two-dimensional (2D) multivariate time series data;
transforming the historical 2D multivariate time series data into a three-dimensional (3D) temporal tensor, wherein the 3D temporal tensor includes a time-based geometric object represented by an array of components that are functions of coordinates of a space;
training one or more deep volumetric 3D convolutional neural networks (CNNs), utilizing the 3D temporal tensor; and
predicting future values for additional multivariate time series data, utilizing the one or more trained deep volumetric 3D CNNs.