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 |
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.
|