# US 2021/0241929 A1 · Analyzing Time Series Data for Sets of Devices Using Machine Learning Techniques > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2021/0241929 A1 - Document ID: US20210241929A1 - Status: Published application - Kind: A1 - Application number: 16/778,456 - Filed: 2020-01-31 - Published: 2021-08-05 - Earliest claimed: 2020-01-31 - Assignees: EMC IP Holding Company LLC - Applicants: EMC IP Holding Company LLC - Inventors: Rahul Vishwakarma; Shelesh Chopra; Gopal Singh; Sujan Kumar Shetty - Web page: https://patentgrep.com/patent/US20210241929A1 ## CPC - G06F 17/17: Function evaluation by approximation methods, e.g. inter- or extrapolation, smoothing, least mean square method ({G06F17/18 takes precedence } ; interpolation for numerical control G05B19/18) - G06F 17/11: for solving equations {, e.g. nonlinear equations, general mathematical optimization problems (optimization specially adapted for a specific administrative, business or logistic context G06Q10/04)} - G06F 17/16: Matrix or vector computation {, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization (matrix transposition G06F7/78)} - G06F 17/18: 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)} - G06N 20/00: Machine learning - G16Y 40/20: Analytics; Diagnosis - G16Y 40/35: Management of things, i.e. controlling in accordance with a policy or in order to achieve specified objectives - H04W 84/18: Self-organising networks, e.g. ad-hoc networks or sensor networks - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G16Y: INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT] - H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72) ## 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. ## Claims ### Claim 1 A computer-implemented method comprising: 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; wherein the method is performed by at least one processing device comprising a processor coupled to a memory. ### Claim 2 (depends on 1) The computer-implemented method of claim 1, wherein the one or more machine learning techniques comprise at least one segmented regression technique. ### Claim 3 (depends on 1) 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. ### Claim 4 (depends on 1) The computer-implemented method of claim 1, wherein performing the one or more automated actions comprises outputting a visualization of the at least one generated data forecast and the one or more computed qualifying values via a graphical user interface. ### Claim 5 (depends on 1) 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. ### Claim 6 (depends on 1) 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. ### Claim 7 (depends on 1) 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. ### Claim 8 (depends on 1) 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. ### Claim 9 (depends on 1) The computer-implemented method of claim 1, wherein the multiple devices comprise one or more Internet of Things devices. ### Claim 10 (depends on 1) The computer-implemented method of claim 1, wherein the multiple devices comprise one or more storage devices. ### Claim 11 (depends on 1) The computer-implemented method of claim 1, wherein the at least one data forecast pertains to anomaly detection. ### Claim 12 (depends on 1) The computer-implemented method of claim 1, wherein the at least one data forecast pertains to capacity utilization. ### Claim 13 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; 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. ### Claim 14 (depends on 13) The non-transitory processor-readable storage medium of claim 13, wherein the one or more machine learning techniques comprise at least one segmented regression technique integrated with a greedy algorithm. ### Claim 15 (depends on 13) The non-transitory processor-readable storage medium of claim 13, wherein the one or more qualifying values comprise at least one confidence value attributed to the at least one generated data forecast. ### Claim 16 (depends on 13) The non-transitory processor-readable storage medium of claim 13, 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. ### Claim 17 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; 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. ### Claim 18 (depends on 17) The apparatus of claim 17, wherein the one or more machine learning techniques comprise at least one segmented regression technique integrated with a greedy algorithm. ### Claim 19 (depends on 17) The apparatus of claim 17, wherein the one or more qualifying values comprise at least one confidence value attributed to the at least one generated data forecast. ### Claim 20 (depends on 17) The apparatus of claim 17, 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.