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Prediction method and device

A forecasting method and forecasting model technology, applied in the field of machine learning, can solve problems such as difficult to predict the number of orders, and achieve the effect of improving forecasting accuracy

Pending Publication Date: 2019-11-08
北京航天智造科技发展有限公司
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Problems solved by technology

Due to the characteristics of unknown, random, discrete and irregular repeatability in the generation of Internet orders, it is difficult to use traditional mathematical models to predict the number of orders

Method used

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Embodiment Construction

[0025] The implementation of the present application will be described in detail below with reference to the accompanying drawings and examples, so as to fully understand and implement the implementation process of how the present application uses technical means to solve technical problems and achieve technical effects.

[0026] In the process of studying this disclosure, the inventor found that there are problems in the existing technology: due to the characteristics of unknown, random, discrete and irregular repeatability of Internet orders, it is difficult to use traditional mathematical models to predict quantity of order.

[0027] In view of this, the inventor proposed an idea, which integrates clustering algorithm and BP neural network algorithm to predict orders. Compared with only using BP neural network algorithm to predict orders, the fusion of clustering algorithm and BP neural network algorithm The method is more accurate and the precision is higher.

[0028] see...

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Abstract

The invention discloses a prediction method, and the method comprises the steps: obtaining initial sample order data, carrying out the denoising of the initial sample order data, and obtaining targetsample order data; clustering the order quantity of the target sample order data to obtain order quantity clusters of the target sample data in K time periods; for each time period, inputting the first order quantity of the order quantity cluster in the time period into a preset neural network model for training, and obtaining an order prediction model corresponding to the time period according tothe second order quantity of the order quantity cluster; obtaining the number of to-be-predicted orders containing a plurality of order numbers, carrying out the clustering of the plurality of ordernumbers, obtaining the order number clustering of K time periods, inputting the order number clustering of the time period into the order prediction model corresponding to the time period for each time period, and obtaining the order prediction number corresponding to each time period; and in combination with the order prediction quantity corresponding to each time period, determining a final order prediction quantity.

Description

technical field [0001] The present application belongs to the technical field of machine learning, and in particular, relates to a prediction method and device. Background technique [0002] With the continuous development of Internet technology, big data technology, artificial intelligence technology and other technologies, Internet-based electronic trade business has also developed rapidly. Order data and information are piled up at an alarming rate, and the forecast of order quantity has become a hot research and concern of domestic and foreign scholars and enterprise developers. Due to the characteristics of unknown, random, discrete and irregular repeatability in the generation of Internet orders, it is difficult to use traditional mathematical models to predict the number of orders. Contents of the invention [0003] In view of this, the technical problem to be solved by this application is to provide a forecasting method and device for realizing intelligent forecas...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q30/06G06K9/62G06N3/08
CPCG06Q10/04G06Q30/0633G06N3/084G06F18/23213
Inventor 刘哲邹萍刘阳
Owner 北京航天智造科技发展有限公司
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