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US Patent 10025753 Computer-implemented systems and methods for time series exploration

Patent 10025753 was granted and assigned to Sas (company) on July, 2018 by the United States Patent and Trademark Office.

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Contents

Is a
Patent
Patent

Patent attributes

Patent Applicant
Sas (company)
Sas (company)
Current Assignee
Sas (company)
Sas (company)
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
10025753
Patent Inventor Names
Udo V. Sglavo0
Jerzy Michal Brzezicki0
Michael James Leonard0
Sujatha Pothireddy0
Edward Tilden Blair0
Date of Patent
July 17, 2018
Patent Application Number
15890013
Date Filed
February 6, 2018
Patent Citations Received
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US Patent 10649750 Automated exchanges of job flow objects between federated area and external storage space
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US Patent 10650045 Staged training of neural networks for improved time series prediction performance
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US Patent 10650046 Many task computing with distributed file system
0
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US Patent 10657107 Many task computing with message passing interface
0
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US Patent 10685283 Demand classification based pipeline system for time-series data forecasting
0
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US Patent D898059 Display screen or portion thereof with graphical user interface
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US Patent 11556774 Method and system for forecasting in sparse data streams via dense data streams
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US Patent 10795935 Automated generation of job flow definitions
0
...
Patent Primary Examiner
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William Titcomb
Patent abstract

Systems and methods are provided for analyzing unstructured time stamped data. A distribution of time-stamped data is analyzed to identify a plurality of potential time series data hierarchies for structuring the data. An analysis of a potential time series data hierarchy may be performed. The analysis of the potential time series data hierarchies may include determining an optimal time series frequency and a data sufficiency metric for each of the potential time series data hierarchies. One of the potential time series data hierarchies may be selected based on a comparison of the data sufficiency metrics. Multiple time series may be derived in a single-read pass according to the selected time series data hierarchy. A time series forecast corresponding to at least one of the derived time series may be generated.

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