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US 2023/0274162 A1Published application
Demand forecasting system, learning system, and demand forecasting method
AssigneeToyota Jidosha Kabushiki Kaisha
InventorsTomotaka SUGAI, Yoshiumi KAWAMURA, Makoto TANIGUCHI
Life of this patent
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Abstract
A demand forecasting system obtains first forecasting data indicating a representative demand forecasting value of a provision target for each cluster by executing a time series clustering process, extracting trend component data for each cluster, and calculating pattern data. The demand forecasting system obtains second forecasting data that is data indicating a forecasting value of a difference for each provision target and obtains third forecasting data indicating a demand forecasting value for each provision target.
Claims11
A demand forecasting system comprising a processor configured to:
- input, regarding a provision target that is a commodity or a service, actual result data including the number of time series actual demand results for each of a plurality of types of provision targets and execute a time series clustering process on the input actual result data for each provision target;
- execute, for each cluster classified by the time series clustering process, an extraction process of extracting trend component data of the cluster from the actual result data;
- execute, for each cluster, a pattern calculation process of calculating pattern data indicating a demand pattern for a predetermined period by performing a statistical process on first data that is data remaining after the trend component data is extracted from the actual result data for the predetermined period;
- execute, for each cluster, a first forecasting process of obtaining first forecasting data indicating a representative demand forecasting value of the provision target belonging to the cluster for each cluster by calculating a sum of the pattern data and the trend component data;
- input, for each provision target, a difference between the actual result data of the provision target and the first forecasting data for the cluster to which the provision target belongs, in a learning model in which a regression learning is performed on the difference for the provision target, and execute a second forecasting process of obtaining second forecasting data that is data indicating a forecasting value of the difference for each provision target;
- and
- obtain third forecasting data that is data indicating a demand forecasting value for each provision target by calculating a sum of the first forecasting data and the second forecasting data for each provision target.
4 more elements
A learning system comprising a processor configured to:
- input, regarding a provision target that is a commodity or a service, a difference between actual result data including the number of time series actual demand results for each of a plurality of types of provision targets and first forecasting data that is data indicating a representative demand forecasting value, which is obtained for each cluster classified by executing a time series clustering process on the actual result data for each provision target, for a cluster to which the provision target belongs, in a regression learning model;
- and
- execute a regression learning of the regression learning model and update the regression learning model such that the regression learning model becomes a model that inputs the difference and outputs a forecasting value of the difference for a predetermined period during operation,
- wherein the first forecasting data is obtained, as data indicating a representative demand forecasting value of the provision target belonging to the cluster for each cluster,
- by executing, for each cluster, an extraction process of extracting trend component data of the cluster from the actual result data,
- by executing, for each cluster, a pattern calculation process of calculating pattern data indicating a demand pattern for a predetermined period by performing a statistical process on first data that is data remaining after the trend component data is extracted from the actual result data for the predetermined period, and
- by executing, for each cluster, a first forecasting process of calculating a sum of the pattern data and the trend component data.
4 more elements
A demand forecasting method comprising:
- inputting, regarding a provision target that is a commodity or a service, actual result data including the number of time series actual demand results for each of a plurality of types of provision targets and executing a time series clustering process on the input actual result data for each provision target;
- executing, for each cluster classified by the time series clustering process, an extraction process of extracting trend component data of the cluster from the actual result data;
- executing, for each cluster, a pattern calculation process of calculating pattern data indicating a demand pattern for a predetermined period by performing a statistical process on first data that is data remaining after the trend component data is extracted from the actual result data for the predetermined period;
- executing, for each cluster, a first forecasting process of obtaining first forecasting data indicating a representative demand forecasting value of the provision target belonging to the cluster for each cluster by calculating a sum of the pattern data and the trend component data;
- inputting, for each provision target, a difference between the actual result data of the provision target and the first forecasting data for the cluster to which the provision target belongs, in a learning model in which a regression learning is performed on the difference for the provision target, and executing a second forecasting process of obtaining second forecasting data that is data indicating a forecasting value of the difference for each provision target;
- and
- obtaining third forecasting data that is data indicating a demand forecasting value for each provision target by calculating a sum of the first forecasting data and the second forecasting data for each provision target.
4 more elements
Dependencies are inferred from the claim text.