Performing multivariate time series prediction with three-dimensional transformations
AssigneeInternational Business Machines Corporation
InventorsMu Qiao, Yuya Jeremy Ong, Divyesh Jadav
Life of this patent
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Abstract
A computer-implemented method according to one embodiment includes receiving historical two-dimensional (2D) multivariate time series data; transforming the historical 2D multivariate time series data into a three-dimensional (3D) temporal tensor; 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.
Claims19
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.
A computer program product for performing multivariate time series prediction with three-dimensional transformations, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to:
- receive, utilizing the processor, historical two-dimensional (2D) multivariate time series data;
- transform, utilizing the processor, the historical 2D multivariate time series data into a three-dimensional (3D) temporal tensor, wherein the transforming includes dividing the historical 2D multivariate time series data into a plurality of 2D temporal slices, wherein the transforming includes stacking the plurality of 2D temporal slices to create the 3D temporal tensor;
- train, utilizing the processor, one or more deep volumetric 3D convolutional neural networks (CNNs), utilizing the 3D temporal tensor;
- and
- predict future values for additional multivariate time series data, utilizing the processor and the one or more trained deep volumetric 3D CNNs.
A system, comprising:
- a hardware processor;
- hardware memory coupled to the processor;
- and
- logic integrated with the processor, executable by the processor, or integrated with and executable by the processor, the logic being configured to:
- receive historical two-dimensional (2D) multivariate time series data;
- transform 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;
- train one or more deep volumetric 3D convolutional neural networks (CNNs), utilizing the 3D temporal tensor;
- and
- predict future values for additional multivariate time series data, utilizing the one or more trained deep volumetric 3D CNNs.
Dependencies are inferred from the claim text.