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Commodity demand prediction system based on attention mechanism

A prediction system and attention technology, applied in prediction, computer components, biological neural network models, etc., can solve the problem that the accuracy of prediction results cannot be satisfactory.

Pending Publication Date: 2021-06-25
深圳鸿禧云上科技有限公司
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Even with convolutional neural networks or recurrent neural networks, the accuracy of prediction results is still not satisfactory

Method used

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  • Commodity demand prediction system based on attention mechanism

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Embodiment Construction

[0018] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiment of the application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiment of the application. Obviously, the described embodiment is only It is an embodiment of a part of the application, but not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the scope of protection of the present application.

[0019] It should be noted that the terms "first", "second", etc. in the description and claims of the present application and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific sequence or sequence. It should be understood that the data so used may be interchanged under appropriate c...

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Abstract

The invention discloses a commodity demand prediction system based on an attention mechanism, which comprises a prediction module, a prediction display module, a hybrid neural network module, an attention mechanism module and a time sequence module. The hybrid neural network module is internally provided with a signal receiving unit, a convolutional neural network module and a recurrent neural network module. A first grabbing module and a first definition module are arranged in the convolutional neural network module, a second grabbing module and a second definition module are arranged in the recurrent neural network module, and an assignment unit and an assignment conveying unit are arranged in the attention mechanism module. A short-term feature unit, a local variable unit, a long-term feature unit and a global macroscopic variable unit are arranged in the time sequence module, and the convolutional neural network module is connected with the short-term feature unit and the local variable unit. The invention provides a hybrid neural network commodity demand prediction system based on an attention mechanism.

Description

technical field [0001] The present application relates to a forecasting system, in particular to a forecasting system for commodity demand based on an attention mechanism. Background technique [0002] The attention mechanism is an algorithm proposed in 2017 to improve the language learning ability of the language model. The attention mechanism can not only quantify the correlation between different words on a single sentence, but also quantify the correlation between different words on multiple sentences. Let the final prediction result focus more on words with strong correlation, while words with weak correlation have less influence on the final prediction result. [0003] Commodity demand forecasting is an important performance under time series forecasting. Traditional time series models such as autoregressive models, moving average models, and time series decomposition models perform poorly when dealing with multidimensional time series and long-term correlation time se...

Claims

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Application Information

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IPC IPC(8): G06N3/04G06Q10/04G06K9/62
CPCG06Q10/04G06N3/045G06F18/214
Inventor 陈智鹰周梓晔马向东
Owner 深圳鸿禧云上科技有限公司
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