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Stock prediction method, system and device based on affinity propagation and medium

A technology of neighbor propagation and prediction method, applied in prediction, character and pattern recognition, instruments and other directions, can solve the problems of "overfitting, inability to parallel processing, slow learning speed, etc., to improve the prediction accuracy, improve the The speed of training, the effect of improving efficiency

Inactive Publication Date: 2019-10-18
WUHAN INSTITUTE OF TECHNOLOGY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Commonly used algorithms include: BP neural network algorithm, RNN cyclic neural network algorithm and LSTM neural network algorithm, etc. Among them, BP neural network algorithm has strong nonlinear mapping ability and self-learning ability, but the learning speed is slow and prone to " Overfitting” phenomenon; the RNN cyclic neural network algorithm will not only learn the information at the current moment, but also rely on the previous sequence information, but when the distance increases, the RNN becomes unable to connect relevant information; the neurons in the LSTM neural network algorithm have special The gate structure can learn long-distance dependencies, but it takes up a lot of memory and cannot be processed in parallel

Method used

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  • Stock prediction method, system and device based on affinity propagation and medium
  • Stock prediction method, system and device based on affinity propagation and medium
  • Stock prediction method, system and device based on affinity propagation and medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0084] Embodiment one, as figure 1 As shown, a stock prediction method based on neighbor propagation includes the following steps:

[0085] S1: Obtain multiple historical transaction data of multiple stocks within a preset time, process the feature vector set in the historical transaction data of any one of the stocks, and obtain the target feature of the corresponding one of the stocks collection of vectors;

[0086] S2: According to the target feature vector set of a corresponding stock obtained in S1, directly obtain a plurality of target feature vector sets corresponding to other remaining stocks in S1; S3: Use neighbor propagation method, performing a neighbor propagation clustering analysis on the historical transaction data corresponding to all the stocks according to the set of all target feature vectors to obtain a clustering result;

[0087] S4: According to the clustering results, select a preset number of sample stocks in the clusters of the stocks to be predicte...

Embodiment 2

[0156] Embodiment two, such as Figure 5 As shown, a stock prediction system based on neighbor propagation, including data acquisition module, data processing module, cluster analysis module, model training and construction module and prediction module;

[0157] The data acquisition module is used to acquire a plurality of historical transaction data of a plurality of stocks within a preset time;

[0158] The data processing module is configured to process the set of feature vectors in the historical transaction data of any one of the stocks to obtain a set of target feature vectors corresponding to one of the stocks;

[0159] The clustering analysis module is used to directly obtain the one-to-one correspondence with the other remaining stocks in the data processing module according to the target feature vector set of the corresponding one of the stocks obtained by the data processing module. A plurality of the target feature vector sets; it is also used to perform neighbor ...

Embodiment 3

[0163] Embodiment 3. Based on Embodiment 1 and Embodiment 2, this embodiment also discloses a stock prediction device based on neighbor propagation, which includes a processor, a memory, and is stored in the memory and can run on the processor. A computer program that, when run, implements the figure 1 The specific steps from S1 to S5 are shown.

[0164] By storing the computer program on the memory and running it on the processor, the prediction of the rising and falling trend of the stock of the present invention is realized. Based on the nearest neighbor propagation clustering method and the support vector machine training method, the calculation amount is low and the calculation time is short. It occupies less memory and has high parallelism. The stock trend prediction model obtained can accurately predict the ups and downs of the stocks to be predicted, and significantly improves the prediction accuracy.

[0165] This embodiment also provides a computer storage medium, w...

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Abstract

The invention relates to a stock prediction method, system and device based on affinity propagation, and a medium, and the method comprises the steps: obtaining the historical transaction data of a plurality of stocks, and processing the historical transaction data of any one stock to obtain a target feature vector set of the stock; directly obtaining target feature vector sets of other remainingstocks according to the target feature vector set of the stock; performing affinity propagation clustering analysis on all historical transaction data according to all the target feature vector sets by adopting an affinity propagation method to obtain a clustering result; selecting a preset number of sample stocks in the cluster to which the to-be-predicted stocks belong according to the clustering result, and obtaining a stock trend prediction model according to the target feature vector set and historical transaction data corresponding to the sample stocks based on a support vector machine training method; and predicting the to-be-predicted stock according to the stock trend prediction model to obtain a prediction result. The invention provided by the invention has the advantages of relatively low operand, relatively short operation time, small occupied memory, high parallelism and high prediction accuracy.

Description

technical field [0001] The present invention relates to the technical field of stock ups and downs trend prediction in a specific time period, in particular to a stock prediction method, system, device and medium based on neighbor propagation. Background technique [0002] The stock market is an important place for the optimal allocation of capital resources, and mastering its changing laws is not only a dream for investors, but also has important practical significance for the research and management of the macro national economy. Since the factors affecting the stock price include internal factors of the enterprise, economic factors, institutional factors and people's psychological factors, etc., and the degree and mode of influence of various factors are different, it is very difficult to accurately predict the stock price. At present, stock price forecasting is mainly to predict the rising and falling trends of stocks. [0003] With the development of Internet technolog...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06Q10/04G06Q40/04
CPCG06Q40/04G06Q10/04G06F18/24143G06F18/2411G06F18/214
Inventor 黄巍胡迪易雪蓉
Owner WUHAN INSTITUTE OF TECHNOLOGY
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