Analyzing time series data for sets of devices using machine learning techniques

Patent metadata

Field	Value
Publication number	US-11663290-B2
Application number	US-202016778456-A
Country	US
Kind code	B2
Filing date	Jan 31, 2020
Priority date	Jan 31, 2020
Publication date	May 30, 2023
Grant date	May 30, 2023

Abstract

Methods, apparatus, and processor-readable storage media for analyzing time series data for sets of devices using machine learning techniques are provided herein. An example computer-implemented method includes processing time series data from multiple devices; generating at least one data forecast by applying, in response to a request from at least one user, one or more machine learning techniques to at least a portion of the processed time series data; computing one or more qualifying values attributable to the at least one generated data forecast by providing the at least one generated data forecast and the at least a portion of the processed time series data to a conformal prediction framework; and performing one or more automated actions based at least in part on the at least one generated data forecast and the one or more computed qualifying values.

First claim

What is claimed is:

1. A computer-implemented method comprising:
processing time series data from multiple devices, wherein the multiple devices comprise one or more Internet of Things devices;
generating at least one data forecast by applying, in response to a request from at least one user, one or more machine learning techniques to at least a portion of the processed time series data;
computing one or more qualifying values attributable to the at least one generated data forecast by providing the at least one generated data forecast and the at least a portion of the processed time series data to a conformal prediction framework; and
performing one or more automated actions based at least in part on the at least one generated data forecast and the one or more computed qualifying values, wherein performing the one or more automated actions comprises outputting, in response to at least one user request for data forecast information submitted via at least one interactive graphical user interface, at least one visualization of at least a portion of the at least one generated data forecast and at least a portion of the one or more computed qualifying values via the at least one interactive graphical user interface;
wherein the method is performed by at least one processing device comprising a processor coupled to a memory.

2. The computer-implemented method of claim 1 , wherein the one or more machine learning techniques comprise at least one segmented regression technique.

3. The computer-implemented method of claim 1 , wherein the one or more machine learning techniques comprise at least one segmented regression technique integrated with a greedy algorithm.

4. The computer-implemented method of claim 1 , wherein the one or more qualifying values comprise at least one confidence value attributed to the at least one generated data forecast.

5. The computer-implemented method of claim 1 , wherein the one or more qualifying values comprise at least one credibility value indicating quality of the at least a portion of the processed time series data used in generating the at least one data forecast.

6. The computer-implemented method of claim 1 , wherein processing the time series data comprises reformatting at least a portion of the time series data from the multiple devices.

7. The computer-implemented method of claim 1 , further comprising:
storing the processed time series data in at least one time series database which stores information pertaining to multiple variables derived from the processed time series data.

8. The computer-implemented method of claim 1 , wherein the multiple devices comprise one or more storage devices.

9. The computer-implemented method of claim 1 , wherein the at least one data forecast pertains to anomaly detection.

10. The computer-implemented method of claim 1 , wherein the at least one data forecast pertains to capacity utilization.

11. A non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device:
to process time series data from multiple devices, wherein the multiple devices comprise one or more Internet of Things devices;
to generate at least one data forecast by applying, in response to a request from at least one user, one or more machine learning techniques to at least a portion of the processed time series data;
to compute one or more qualifying values attributable to the at least one generated data forecast by providing the at least one generated data forecast and the at least a portion of the processed time series data to a conformal prediction framework; and
to perform one or more automated actions based at least in part on the at least one generated data forecast and the one or more computed qualifying values, wherein performing the one or more automated actions comprises outputting, in response to at least one user request for data forecast information submitted via at least one interactive graphical user interface, at least one visualization of at least a portion of the at least one generated data forecast and at least a portion of the one or more computed qualifying values via the at least one interactive graphical user interface.

12. The non-transitory processor-readable storage medium of claim 11 , wherein the one or more machine learning techniques comprise at least one segmented regression technique integrated with a greedy algorithm.

13. The non-transitory processor-readable storage medium of claim 11 , wherein the one or more qualifying values comprise at least one confidence value attributed to the at least one generated data forecast.

14. The non-transitory processor-readable storage medium of claim 11 , wherein the one or more qualifying values comprise at least one credibility value indicating quality of the at least a portion of the processed time series data used in generating the at least one data forecast.

15. An apparatus comprising:
at least one processing device comprising a processor coupled to a memory;
the at least one processing device being configured:
to process time series data from multiple devices, wherein the multiple devices comprise one or more Internet of Things devices;
to generate at least one data forecast by applying, in response to a request from at least one user, one or more machine learning techniques to at least a portion of the processed time series data;
to compute one or more qualifying values attributable to the at least one generated data forecast by providing the at least one generated data forecast and the at least a portion of the processed time series data to a conformal prediction framework; and
to perform one or more automated actions based at least in part on the at least one generated data forecast and the one or more computed qualifying values, wherein performing the one or more automated actions comprises outputting, in response to at least one user request for data forecast information submitted via at least one interactive graphical user interface, at least one visualization of at least a portion of the at least one generated data forecast and at least a portion of the one or more computed qualifying values via the at least one interactive graphical user interface.

16. The apparatus of claim 15 , wherein the one or more machine learning techniques comprise at least one segmented regression technique integrated with a greedy algorithm.

17. The apparatus of claim 15 , wherein the one or more qualifying values comprise at least one confidence value attributed to the at least one generated data forecast.

18. The apparatus of claim 15 , wherein the one or more qualifying values comprise at least one credibility value indicating quality of the at least a portion of the processed time series data used in generating the at least one data forecast.

19. The apparatus of claim 15 , wherein the at least one data forecast pertains to anomaly detection.

20. The apparatus of claim 15 , wherein the at least one data forecast pertains to capacity utilization.

Assignees

• Emc Ip Holding Co Llc

Inventors

• Vishwakarma Rahul
• Chopra Shelesh
• Singh Gopal
• Shetty Sujan Kumar

Classifications

• G16Y40/20

  Analytics; Diagnosis · CPC title

• G06N20/00 Primary

  Machine learning · CPC title

• G06F17/18

  for evaluating statistical data {, e.g. average values, frequency distributions, probability functions, regression analysis (forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04)} · CPC title

• G06F17/11

  for solving equations {, e.g. nonlinear equations, general mathematical optimization problems (optimization specially adapted for a specific administrative, business or logistic context G06Q10/04)} · CPC title

• G06F17/16

  Matrix or vector computation {, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization (matrix transposition G06F7/78)} · CPC title