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Product grain retail prediction method and apparatus thereof

A prediction method and technology of prediction device, applied in the field of data analysis, can solve the problem of insufficient accuracy of prediction results, and achieve the effect of improving prediction accuracy and increasing dimensions

Inactive Publication Date: 2017-05-24
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  • Description
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Problems solved by technology

[0006] Since the above-mentioned method of calculating the state transition matrix by using the Markov chain through the abstract sales state only considers two or three sales states as the basis, the accuracy of the prediction result is not high enough

Method used

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  • Product grain retail prediction method and apparatus thereof
  • Product grain retail prediction method and apparatus thereof
  • Product grain retail prediction method and apparatus thereof

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Embodiment Construction

[0056] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only It is a part of embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0057] In order to improve the prediction accuracy of product grain sales, an embodiment of the present invention provides a product grain retail sales forecast method, such as figure 1 shown, including steps:

[0058] S11. Construct an attribute set according to the artificial attributes related to the sales results in the product grain sales p...

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Abstract

The invention discloses a product grain retail prediction method and an apparatus thereof. The method comprises the following steps of according to man-made attributes related to a sale result in a product grain sale process, constructing an attribute set; according to a difference of attribute states of the man-made attributes, generating an attribute state set of the attribute set; according to the attribute state set, constructing a man-made attribute Markov chain model, and generating an attribute state transfer probability matrix; according to a sale state set, constructing a sale state Markov chain model, and generating a sale state transfer probability matrix; acquiring an influence factor matrix between the attribute states and a sale state; and according to the influence factor matrix, calculating a transfer probability of a sale state value. In an embodiment of the invention, an influence of the man-made attributes of a product grain on the sale state is considered and dimensions of a sale prediction model are increased so that prediction accuracy of product grain sale can be effectively increased.

Description

technical field [0001] The invention relates to the field of data analysis, in particular to a method and device for predicting retail sales of products and grains. Background technique [0002] As people's emphasis on food safety continues to increase, the frequency of product replacements is also increasing in response to changing factors such as market demand, seasons, and policies. [0003] In order to avoid losses caused by changes in the market and make the upgrading of grain products more adaptable to changes in the market, more and more attention has been paid to effective forecasting of grain retail sales. [0004] In the prior art, most of the methods of product grain retail forecasting use data statistics to assist data analysis for forecasting, and solve the problem of sales forecasting by abstracting the sales status and using the Markov chain to calculate the state transition matrix. [0005] The inventor has found through research that at least the following ...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q30/02
Inventor 孙科武于志强肖天柱
Owner AEROSPACE INFORMATION
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