COMPUTER SYSTEM AND LEARNING METHOD OF PREDICTION MODEL
To generate a model which can achieve an item demand prediction with a high degree of accuracy.SOLUTION: A computer system holds model information and actual result information. The model information includes information on a first model for predicting demand of a first item. The computer system: ca...
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| creator | MATSUMOTO NORIKO UEKI TAKAO OJIRO DAICHI YAMAMOTO RYU KAMOSHITA RYOTA LIU QI WATANABE TAKASHI CHEN CHENG |
| description | To generate a model which can achieve an item demand prediction with a high degree of accuracy.SOLUTION: A computer system holds model information and actual result information. The model information includes information on a first model for predicting demand of a first item. The computer system: calculates a demand prediction of a second item in a first period, by using the first model; acquires a demand actual result of the second item in a second period having the same time width as the first period, from the actual result information; identifies a first analysis period within the first period and a second analysis period within the second period, in which an error of a shipping amount is large, on the basis of the demand prediction of the second item and the demand actual result of the second item; analyzes a generation period of a peak of the shipping amount of the first item in the first analysis period, and a generation period of a peak of the shipping amount of the second item in the second analysis period; and executes learning of a model including a feature amount defined on the basis of a result of the analysis.SELECTED DRAWING: Figure 2
【課題】精度の高いアイテムの需要予測を実現可能なモデルを生成する。【解決手段】計算機システムはモデル情報及び実績情報を保持する。モデル情報は第１アイテムの需要を予測する第１モデルの情報を含む。計算機システムは、第１モデルを用いて、第１期間における第２アイテムの需要予測を算出し、実績情報から、第１期間と同じ時間幅の第２期間における第２アイテムの需要実績を取得し、第２アイテムの需要予測及び第２アイテムの需要実績に基づいて、出荷量の誤差が大きい、第１期間内の第１解析期間及び第２期間内の第２解析期間を特定し、第１解析期間における第１アイテムの出荷量のピークの発生時期と、第２解析期間における第２アイテムの出荷量のピークの発生時期とを解析し、解析の結果に基づいて定義された特徴量を含むモデルの学習を実行する。【選択図】 図２ |
| format | Patent |
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【課題】精度の高いアイテムの需要予測を実現可能なモデルを生成する。【解決手段】計算機システムはモデル情報及び実績情報を保持する。モデル情報は第１アイテムの需要を予測する第１モデルの情報を含む。計算機システムは、第１モデルを用いて、第１期間における第２アイテムの需要予測を算出し、実績情報から、第１期間と同じ時間幅の第２期間における第２アイテムの需要実績を取得し、第２アイテムの需要予測及び第２アイテムの需要実績に基づいて、出荷量の誤差が大きい、第１期間内の第１解析期間及び第２期間内の第２解析期間を特定し、第１解析期間における第１アイテムの出荷量のピークの発生時期と、第２解析期間における第２アイテムの出荷量のピークの発生時期とを解析し、解析の結果に基づいて定義された特徴量を含むモデルの学習を実行する。【選択図】 図２</description><language>eng ; jpn</language><subject>CALCULATING ; COMPUTING ; COUNTING ; DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES ; PHYSICS ; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR</subject><creationdate>2023</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&date=20230728&DB=EPODOC&CC=JP&NR=2023104145A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25563,76418</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20230728&DB=EPODOC&CC=JP&NR=2023104145A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>MATSUMOTO NORIKO</creatorcontrib><creatorcontrib>UEKI TAKAO</creatorcontrib><creatorcontrib>OJIRO DAICHI</creatorcontrib><creatorcontrib>YAMAMOTO RYU</creatorcontrib><creatorcontrib>KAMOSHITA RYOTA</creatorcontrib><creatorcontrib>LIU QI</creatorcontrib><creatorcontrib>WATANABE TAKASHI</creatorcontrib><creatorcontrib>CHEN CHENG</creatorcontrib><title>COMPUTER SYSTEM AND LEARNING METHOD OF PREDICTION MODEL</title><description>To generate a model which can achieve an item demand prediction with a high degree of accuracy.SOLUTION: A computer system holds model information and actual result information. The model information includes information on a first model for predicting demand of a first item. The computer system: calculates a demand prediction of a second item in a first period, by using the first model; acquires a demand actual result of the second item in a second period having the same time width as the first period, from the actual result information; identifies a first analysis period within the first period and a second analysis period within the second period, in which an error of a shipping amount is large, on the basis of the demand prediction of the second item and the demand actual result of the second item; analyzes a generation period of a peak of the shipping amount of the first item in the first analysis period, and a generation period of a peak of the shipping amount of the second item in the second analysis period; and executes learning of a model including a feature amount defined on the basis of a result of the analysis.SELECTED DRAWING: Figure 2
【課題】精度の高いアイテムの需要予測を実現可能なモデルを生成する。【解決手段】計算機システムはモデル情報及び実績情報を保持する。モデル情報は第１アイテムの需要を予測する第１モデルの情報を含む。計算機システムは、第１モデルを用いて、第１期間における第２アイテムの需要予測を算出し、実績情報から、第１期間と同じ時間幅の第２期間における第２アイテムの需要実績を取得し、第２アイテムの需要予測及び第２アイテムの需要実績に基づいて、出荷量の誤差が大きい、第１期間内の第１解析期間及び第２期間内の第２解析期間を特定し、第１解析期間における第１アイテムの出荷量のピークの発生時期と、第２解析期間における第２アイテムの出荷量のピークの発生時期とを解析し、解析の結果に基づいて定義された特徴量を含むモデルの学習を実行する。【選択図】 図２</description><subject>CALCULATING</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES</subject><subject>PHYSICS</subject><subject>SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2023</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZDB39vcNCA1xDVIIjgwOcfVVcPRzUfBxdQzy8_RzV_B1DfHwd1Hwd1MICHJ18XQO8fT3U_D1d3H14WFgTUvMKU7lhdLcDEpuriHOHrqpBfnxqcUFicmpeakl8V4BRgZGxoYGJoYmpo7GRCkCAI0aKI0</recordid><startdate>20230728</startdate><enddate>20230728</enddate><creator>MATSUMOTO NORIKO</creator><creator>UEKI TAKAO</creator><creator>OJIRO DAICHI</creator><creator>YAMAMOTO RYU</creator><creator>KAMOSHITA RYOTA</creator><creator>LIU QI</creator><creator>WATANABE TAKASHI</creator><creator>CHEN CHENG</creator><scope>EVB</scope></search><sort><creationdate>20230728</creationdate><title>COMPUTER SYSTEM AND LEARNING METHOD OF PREDICTION MODEL</title><author>MATSUMOTO NORIKO ; UEKI TAKAO ; OJIRO DAICHI ; YAMAMOTO RYU ; KAMOSHITA RYOTA ; LIU QI ; WATANABE TAKASHI ; CHEN CHENG</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_JP2023104145A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng ; jpn</language><creationdate>2023</creationdate><topic>CALCULATING</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES</topic><topic>PHYSICS</topic><topic>SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR</topic><toplevel>online_resources</toplevel><creatorcontrib>MATSUMOTO NORIKO</creatorcontrib><creatorcontrib>UEKI TAKAO</creatorcontrib><creatorcontrib>OJIRO DAICHI</creatorcontrib><creatorcontrib>YAMAMOTO RYU</creatorcontrib><creatorcontrib>KAMOSHITA RYOTA</creatorcontrib><creatorcontrib>LIU QI</creatorcontrib><creatorcontrib>WATANABE TAKASHI</creatorcontrib><creatorcontrib>CHEN CHENG</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>MATSUMOTO NORIKO</au><au>UEKI TAKAO</au><au>OJIRO DAICHI</au><au>YAMAMOTO RYU</au><au>KAMOSHITA RYOTA</au><au>LIU QI</au><au>WATANABE TAKASHI</au><au>CHEN CHENG</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>COMPUTER SYSTEM AND LEARNING METHOD OF PREDICTION MODEL</title><date>2023-07-28</date><risdate>2023</risdate><abstract>To generate a model which can achieve an item demand prediction with a high degree of accuracy.SOLUTION: A computer system holds model information and actual result information. The model information includes information on a first model for predicting demand of a first item. The computer system: calculates a demand prediction of a second item in a first period, by using the first model; acquires a demand actual result of the second item in a second period having the same time width as the first period, from the actual result information; identifies a first analysis period within the first period and a second analysis period within the second period, in which an error of a shipping amount is large, on the basis of the demand prediction of the second item and the demand actual result of the second item; analyzes a generation period of a peak of the shipping amount of the first item in the first analysis period, and a generation period of a peak of the shipping amount of the second item in the second analysis period; and executes learning of a model including a feature amount defined on the basis of a result of the analysis.SELECTED DRAWING: Figure 2
【課題】精度の高いアイテムの需要予測を実現可能なモデルを生成する。【解決手段】計算機システムはモデル情報及び実績情報を保持する。モデル情報は第１アイテムの需要を予測する第１モデルの情報を含む。計算機システムは、第１モデルを用いて、第１期間における第２アイテムの需要予測を算出し、実績情報から、第１期間と同じ時間幅の第２期間における第２アイテムの需要実績を取得し、第２アイテムの需要予測及び第２アイテムの需要実績に基づいて、出荷量の誤差が大きい、第１期間内の第１解析期間及び第２期間内の第２解析期間を特定し、第１解析期間における第１アイテムの出荷量のピークの発生時期と、第２解析期間における第２アイテムの出荷量のピークの発生時期とを解析し、解析の結果に基づいて定義された特徴量を含むモデルの学習を実行する。【選択図】 図２</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | CALCULATING COMPUTING COUNTING DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES PHYSICS SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR |
| title | COMPUTER SYSTEM AND LEARNING METHOD OF PREDICTION MODEL |
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