DEMAND CLASSIFICATION BASED PIPELINE SYSTEM FOR TIME-SERIES DATA FORECASTING

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 modeling results such as f...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: HALEY, TIMOTHY PATRICK, HODGIN, RON TRAVIS, HELMKAMP, PHILLIP MARK, FRAZIER, MACKLIN CARTER, KIM, SANGMIN, CHIEN, YUNG-HSIN, BRZEZICKI, JERZY MICHAL, TROVERO, MICHELE ANGELO, XIE, JINGRUI, SOLOMONSON, RANDY THOMAS, MILLS, STEVEN CHRISTOPHER, LI, YUE
Format: Patent
Sprache:eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title
container_volume
creator HALEY, TIMOTHY PATRICK
HODGIN, RON TRAVIS
HELMKAMP, PHILLIP MARK
FRAZIER, MACKLIN CARTER
KIM, SANGMIN
CHIEN, YUNG-HSIN
BRZEZICKI, JERZY MICHAL
TROVERO, MICHELE ANGELO
XIE, JINGRUI
SOLOMONSON, RANDY THOMAS
MILLS, STEVEN CHRISTOPHER
LI, YUE
description 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 modeling results such as forecasts of the time series. The pipeline includes a segmentation operation for categorizing the time series into multiple demand classes based on demand characteristics of the time series. The pipeline also includes multiple sub-pipelines corresponding to the multiple demand classes. Each of the sub-pipelines applies a model strategy to the time series in the corresponding demand class. The model strategy is selected from multiple candidate model strategies based on predetermined relationships between the demand classes and the candidate model strategies. The pipeline is executed to determine the modeling results for the time series.
format Patent
fullrecord <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_US2020143246A1</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>US2020143246A1</sourcerecordid><originalsourceid>FETCH-epo_espacenet_US2020143246A13</originalsourceid><addsrcrecordid>eNqNyrEKwjAQgOEuDqK-w4FzoU2L-5lc9CBJS-8cnEqROIkW6vsjgg_g9MPHvy6Co4jJgQ0owp4tKncJjijkoOeeAicCuYpSBN8NoBypFBqYBBwqfpEsinI6bYvVfXoseffrpth7Unsu8_wa8zJPt_zM7_EipjJV3TamPWDd_Hd9AGZeLsM</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>DEMAND CLASSIFICATION BASED PIPELINE SYSTEM FOR TIME-SERIES DATA FORECASTING</title><source>esp@cenet</source><creator>HALEY, TIMOTHY PATRICK ; HODGIN, RON TRAVIS ; HELMKAMP, PHILLIP MARK ; FRAZIER, MACKLIN CARTER ; KIM, SANGMIN ; CHIEN, YUNG-HSIN ; BRZEZICKI, JERZY MICHAL ; TROVERO, MICHELE ANGELO ; XIE, JINGRUI ; SOLOMONSON, RANDY THOMAS ; MILLS, STEVEN CHRISTOPHER ; LI, YUE</creator><creatorcontrib>HALEY, TIMOTHY PATRICK ; HODGIN, RON TRAVIS ; HELMKAMP, PHILLIP MARK ; FRAZIER, MACKLIN CARTER ; KIM, SANGMIN ; CHIEN, YUNG-HSIN ; BRZEZICKI, JERZY MICHAL ; TROVERO, MICHELE ANGELO ; XIE, JINGRUI ; SOLOMONSON, RANDY THOMAS ; MILLS, STEVEN CHRISTOPHER ; LI, YUE</creatorcontrib><description>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 modeling results such as forecasts of the time series. The pipeline includes a segmentation operation for categorizing the time series into multiple demand classes based on demand characteristics of the time series. The pipeline also includes multiple sub-pipelines corresponding to the multiple demand classes. Each of the sub-pipelines applies a model strategy to the time series in the corresponding demand class. The model strategy is selected from multiple candidate model strategies based on predetermined relationships between the demand classes and the candidate model strategies. The pipeline is executed to determine the modeling results for the time series.</description><language>eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; ELECTRIC DIGITAL DATA PROCESSING ; PHYSICS</subject><creationdate>2020</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&amp;date=20200507&amp;DB=EPODOC&amp;CC=US&amp;NR=2020143246A1$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76547</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&amp;date=20200507&amp;DB=EPODOC&amp;CC=US&amp;NR=2020143246A1$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>HALEY, TIMOTHY PATRICK</creatorcontrib><creatorcontrib>HODGIN, RON TRAVIS</creatorcontrib><creatorcontrib>HELMKAMP, PHILLIP MARK</creatorcontrib><creatorcontrib>FRAZIER, MACKLIN CARTER</creatorcontrib><creatorcontrib>KIM, SANGMIN</creatorcontrib><creatorcontrib>CHIEN, YUNG-HSIN</creatorcontrib><creatorcontrib>BRZEZICKI, JERZY MICHAL</creatorcontrib><creatorcontrib>TROVERO, MICHELE ANGELO</creatorcontrib><creatorcontrib>XIE, JINGRUI</creatorcontrib><creatorcontrib>SOLOMONSON, RANDY THOMAS</creatorcontrib><creatorcontrib>MILLS, STEVEN CHRISTOPHER</creatorcontrib><creatorcontrib>LI, YUE</creatorcontrib><title>DEMAND CLASSIFICATION BASED PIPELINE SYSTEM FOR TIME-SERIES DATA FORECASTING</title><description>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 modeling results such as forecasts of the time series. The pipeline includes a segmentation operation for categorizing the time series into multiple demand classes based on demand characteristics of the time series. The pipeline also includes multiple sub-pipelines corresponding to the multiple demand classes. Each of the sub-pipelines applies a model strategy to the time series in the corresponding demand class. The model strategy is selected from multiple candidate model strategies based on predetermined relationships between the demand classes and the candidate model strategies. The pipeline is executed to determine the modeling results for the time series.</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>ELECTRIC DIGITAL DATA PROCESSING</subject><subject>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2020</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNyrEKwjAQgOEuDqK-w4FzoU2L-5lc9CBJS-8cnEqROIkW6vsjgg_g9MPHvy6Co4jJgQ0owp4tKncJjijkoOeeAicCuYpSBN8NoBypFBqYBBwqfpEsinI6bYvVfXoseffrpth7Unsu8_wa8zJPt_zM7_EipjJV3TamPWDd_Hd9AGZeLsM</recordid><startdate>20200507</startdate><enddate>20200507</enddate><creator>HALEY, TIMOTHY PATRICK</creator><creator>HODGIN, RON TRAVIS</creator><creator>HELMKAMP, PHILLIP MARK</creator><creator>FRAZIER, MACKLIN CARTER</creator><creator>KIM, SANGMIN</creator><creator>CHIEN, YUNG-HSIN</creator><creator>BRZEZICKI, JERZY MICHAL</creator><creator>TROVERO, MICHELE ANGELO</creator><creator>XIE, JINGRUI</creator><creator>SOLOMONSON, RANDY THOMAS</creator><creator>MILLS, STEVEN CHRISTOPHER</creator><creator>LI, YUE</creator><scope>EVB</scope></search><sort><creationdate>20200507</creationdate><title>DEMAND CLASSIFICATION BASED PIPELINE SYSTEM FOR TIME-SERIES DATA FORECASTING</title><author>HALEY, TIMOTHY PATRICK ; HODGIN, RON TRAVIS ; HELMKAMP, PHILLIP MARK ; FRAZIER, MACKLIN CARTER ; KIM, SANGMIN ; CHIEN, YUNG-HSIN ; BRZEZICKI, JERZY MICHAL ; TROVERO, MICHELE ANGELO ; XIE, JINGRUI ; SOLOMONSON, RANDY THOMAS ; MILLS, STEVEN CHRISTOPHER ; LI, YUE</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_US2020143246A13</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng</language><creationdate>2020</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>ELECTRIC DIGITAL DATA PROCESSING</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>HALEY, TIMOTHY PATRICK</creatorcontrib><creatorcontrib>HODGIN, RON TRAVIS</creatorcontrib><creatorcontrib>HELMKAMP, PHILLIP MARK</creatorcontrib><creatorcontrib>FRAZIER, MACKLIN CARTER</creatorcontrib><creatorcontrib>KIM, SANGMIN</creatorcontrib><creatorcontrib>CHIEN, YUNG-HSIN</creatorcontrib><creatorcontrib>BRZEZICKI, JERZY MICHAL</creatorcontrib><creatorcontrib>TROVERO, MICHELE ANGELO</creatorcontrib><creatorcontrib>XIE, JINGRUI</creatorcontrib><creatorcontrib>SOLOMONSON, RANDY THOMAS</creatorcontrib><creatorcontrib>MILLS, STEVEN CHRISTOPHER</creatorcontrib><creatorcontrib>LI, YUE</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>HALEY, TIMOTHY PATRICK</au><au>HODGIN, RON TRAVIS</au><au>HELMKAMP, PHILLIP MARK</au><au>FRAZIER, MACKLIN CARTER</au><au>KIM, SANGMIN</au><au>CHIEN, YUNG-HSIN</au><au>BRZEZICKI, JERZY MICHAL</au><au>TROVERO, MICHELE ANGELO</au><au>XIE, JINGRUI</au><au>SOLOMONSON, RANDY THOMAS</au><au>MILLS, STEVEN CHRISTOPHER</au><au>LI, YUE</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>DEMAND CLASSIFICATION BASED PIPELINE SYSTEM FOR TIME-SERIES DATA FORECASTING</title><date>2020-05-07</date><risdate>2020</risdate><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 modeling results such as forecasts of the time series. The pipeline includes a segmentation operation for categorizing the time series into multiple demand classes based on demand characteristics of the time series. The pipeline also includes multiple sub-pipelines corresponding to the multiple demand classes. Each of the sub-pipelines applies a model strategy to the time series in the corresponding demand class. The model strategy is selected from multiple candidate model strategies based on predetermined relationships between the demand classes and the candidate model strategies. The pipeline is executed to determine the modeling results for the time series.</abstract><oa>free_for_read</oa></addata></record>
fulltext fulltext_linktorsrc
identifier
ispartof
issn
language eng
recordid cdi_epo_espacenet_US2020143246A1
source esp@cenet
subjects CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
ELECTRIC DIGITAL DATA PROCESSING
PHYSICS
title DEMAND CLASSIFICATION BASED PIPELINE SYSTEM FOR TIME-SERIES DATA FORECASTING
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-01T23%3A54%3A51IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-epo_EVB&rft_val_fmt=info:ofi/fmt:kev:mtx:patent&rft.genre=patent&rft.au=HALEY,%20TIMOTHY%20PATRICK&rft.date=2020-05-07&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3EUS2020143246A1%3C/epo_EVB%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true