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US Patent 10560313 Pipeline system for time-series data forecasting

Patent 10560313 was granted and assigned to Sas (company) on February, 2020 by the United States Patent and Trademark Office.

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Is a
Patent
Patent
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Patent attributes

Patent Applicant
Sas (company)
Sas (company)
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Current Assignee
Sas (company)
Sas (company)
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Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
105603130
Patent Inventor Names
Sujatha Pothireddy0
Timothy Patrick Haley0
Udo Vincenzo Sglavo0
Jerzy Michal Brzezicki0
Phillip Mark Helmkamp0
Date of Patent
February 11, 2020
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Patent Application Number
164527910
Date Filed
June 26, 2019
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Patent Citations
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US Patent 10169709 Avoiding incompatibility between data and computing processes to enhance computer performance
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US Patent 10169720 Systems and methods for machine learning using classifying, clustering, and grouping time series data
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US Patent 10331490 Scalable cloud-based time series analysis
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US Patent 10366117 Computer-implemented systems and methods for taxonomy development
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US Patent 10338994 Predicting and adjusting computer functionality to avoid failures
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US Patent 10372734 Controlling operation of a machine by performing reconciliation using a distributed cluster of nodes
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US Patent 10025753 Computer-implemented systems and methods for time series exploration
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US Patent 10037305 Computer-implemented systems and methods for time series exploration
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Patent Citations Received
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US Patent 11495210 Acoustic based speech analysis using deep learning models
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US Patent 11556791 Predicting and managing requests for computing resources or other resources
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US Patent 11562388 Machine learned models for items with time-shifts
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US Patent 11620582 Automated machine learning pipeline generation
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US Patent 11688111 Visualization of a model selection process in an automated model selection system
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US Patent 11321205 Enterprise-scale time series graphite backend infrastructure
Patent Primary Examiner
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Ricardo H Castaneyra
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Patent abstract

A pipeline system for time-series data forecasting using a distributed computing environment is disclosed herein. In one example, a pipeline for forecasting time series is generated. The pipeline represents a sequence of operations for processing the time series to produce forecasts. The sequence of operations include model strategy operations for applying various model strategies to the time series to determine error distributions corresponding to the model strategies. The sequence of operations further include a model-strategy comparison operation for determining which of the model strategies is a champion model strategy for the plurality of time series based on the error distributions of the model strategies. The pipeline is executed to determine the champion model strategy for the time series.

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