Demand forecasting method, demand forecasting system, and recording medium
First Claim
1. A method for forecasting demand for a product based on sales results of the product, comprising:
- setting plural models as a neural network;
identifying sales results of a first period;
inputting the identified sales results of the first period to each of the models to make the neural network of each model learn from inputs and produce data as close as possible to sales results of a second period following the first period;
storing a forecast demand value of a predetermined time outputted by each of the neural networks;
selecting a model from the learned neural networks which has a forecast demand value closest to the sales results of the predetermined time; and
inputting latest sales results identified by the learned neural network corresponding to the selected model to forecast demand.
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Abstract
A method and a system for forecasting the demand agreeing with the fluctuation trend of sales results at high and stable precision, without requiring user'"'"'s maintenance, by using a model optimum for grasping the fluctuation trend of sales results, even if the products are diverse, by storing a plurality of models of neural network, for example, a model for forecasting the demand from data of the past several months, a model for forecasting the demand from data of the same period of the previous year, and a model for forecasting the demand from both the latest data and data of the same period of the previous year, and also by feeding sales results into a model of neural network to make it learn by the short period such as by the week, and a recording medium in which is recorded such program.
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Citations
22 Claims
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1. A method for forecasting demand for a product based on sales results of the product, comprising:
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setting plural models as a neural network; identifying sales results of a first period; inputting the identified sales results of the first period to each of the models to make the neural network of each model learn from inputs and produce data as close as possible to sales results of a second period following the first period; storing a forecast demand value of a predetermined time outputted by each of the neural networks; selecting a model from the learned neural networks which has a forecast demand value closest to the sales results of the predetermined time; and inputting latest sales results identified by the learned neural network corresponding to the selected model to forecast demand. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 19)
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13. A computer readable storage media storing a process of forecasting the demand for a product on the basis of the sales results of the product comprising:
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setting plural models as a neural network; identifying sales results of a first period; inputting the identified sales results of the first period to each of the models to make the neural network of each model learn from inputs and produce data as close as possible to the sales results of a second period following the first period; storing a forecast demand value of a predetermined time outputted by each of the neural networks; selecting a model from the learned neural networks which has a demand value closest to the sales results of the predetermined time; and inputting a latest sales results identified by the learned neural network corresponding to the selected model to forecast a demand. - View Dependent Claims (14, 15, 16, 17, 18)
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20. A demand forecasting method comprising:
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creating a plurality of neural network models to forecast demand based on different time periods; identifying sales results of a first period and entering the results into each of the models to allow each model to learn and forecast demand for a second period; comparing the forecast demand from each of the models for the second period with actual sales results to compute an error of each model; and selecting the model with the smallest error.
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21. A computer readable storage medium storing software to implement a demand forecasting method performing;
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creating a plurality of neural network models to forecast demand based on different time periods; identifying sales results of a first period and entering the results into each of the models to allow each Model to learn and forecast demand for a second period; comparing the forecast demand from each of the models for the second period with actual sales results to compute an error of each model; and selecting the model with the smallest error.
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22. A demand forecasting system comprising:
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neural network models forecasting demand based on different time periods; an inputting device inputting sales results of a first period into each of the models; a comparing device comparing a forecast demand from each of the models for a second period with actual sales results to compute an error of each model; and a selecting device selecting the model with the smallest error.
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Specification