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agricultural product price prediction method based on a seq2seq model and a CNN model

A technology for price forecasting and agricultural products, applied in market forecasting, neural learning methods, biological neural network models, etc.

Pending Publication Date: 2019-04-19
GUANGDONG UNIV OF TECH
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AI Technical Summary

Problems solved by technology

[0004] The present invention provides a method for predicting the price of agricultural products based on the seq2seq model and the CNN model in order to overcome the defects of low prediction accuracy of the price of agricultural products in the above-mentioned prior art

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  • agricultural product price prediction method based on a seq2seq model and a CNN model
  • agricultural product price prediction method based on a seq2seq model and a CNN model

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Embodiment 1

[0031] Such as figure 1 Shown, a kind of agricultural product price prediction method based on seq2seq model and CNN model, described method comprises:

[0032] S1: collect the original historical price data of several kinds of agricultural products in a fixed time period T to form the historical price data set M of agricultural products. The historical price data set M of agricultural products includes daily price data, daily average Temperature data, daily average rainfall data, where the unit of T is day, and the daily price data in the agricultural product historical price data set M are preprocessed, and each of the preprocessed agricultural product historical price data set M is The daily price data of agricultural products are processed into price trend images, and labels are added to the price trend images at the same time;

[0033] The horizontal axis of the price trend image in step S1 is time, and the unit length is 1 day, and the vertical axis of the price trend i...

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Abstract

The invention discloses an agricultural product price prediction method based on a seq2seq model and a CNN model, and the method comprises the steps: collecting historical price data of an agricultural product to form a data set M, carrying out the preprocessing of the data set M, converting the preprocessed data into a price trend image, and adding a label; Processing the data set M into multi-dimensional index data to train a seq2seq model; Using the price trend image with the label to train a CNN model; Processing the original historical price data of the to-be-predicted agricultural product into multi-dimensional index data, and inputting the multi-dimensional index data into the trained seq2seq model to obtain a quasi-prediction result Vs; And inputting the price trend image with thelabel into the trained CNN model to obtain a quasi-prediction result Vc, and correcting the prediction result Vs by using the quasi-prediction result Vc to obtain the final prediction price of the to-be-predicted agricultural product. Through double-model mixing, the method has high accuracy for agricultural product price prediction.

Description

technical field [0001] The present invention relates to the field of price prediction of agricultural products, and more specifically, to a method for predicting prices of agricultural products based on a seq2seq model and a CNN model. Background technique [0002] The agricultural product market is an extremely important part of China's market economic system, and the price of agricultural products is the core of the agricultural product market. The large price fluctuations of agricultural products not only lead to price fluctuations, but also bring additional risks to practitioners in the agricultural product market, causing great harm to the stable development of the entire agricultural product market. [0003] However, there are many factors that affect the price fluctuations of agricultural products, such as seasonal factors, climatic factors, changes in the supply and demand of agricultural products, and circulation cost factors, etc., which makes it a challenging task...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q30/02G06Q50/02G06N3/04G06N3/08
CPCG06Q30/0206G06N3/04G06N3/08G06Q50/02
Inventor 王铭锋左亚尧马铎
Owner GUANGDONG UNIV OF TECH
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