scholarly journals Stock Price Trend Prediction Model Based on WNN with Redundant Structure Reduced by RS

2017 ◽  
Author(s):  
Shuili Ren ◽  
Yuanwei Lou ◽  
Lei Lei
Keyword(s):  
Prediction Model ◽  
Stock Price ◽  
Trend Prediction ◽  
Price Trend ◽  
Model Based ◽  
Redundant Structure

scholarly journals A Dual-Attention-Based Stock Price Trend Prediction Model With Dual Features

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 148047-148058 ◽  
Author(s):  
Yingxuan Chen ◽  
Weiwei Lin ◽  
James Z. Wang
Keyword(s):  
Prediction Model ◽  
Stock Price ◽  
Trend Prediction ◽  
Price Trend

Prediction model for stock price trend based on convolution neural network

2020 ◽  
Vol 39 (4) ◽  
pp. 4999-5008
Author(s):  
Hongbo Lin ◽  
Jinghua Zhao ◽  
Shuang Liang ◽  
Huilin Kang
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Stock Price ◽  
Image Features ◽  
Image Feature ◽  
Extraction Ability ◽  
Residual Network ◽  
Trend Prediction ◽  
Prediction Ability ◽  
Price Trend

Aiming at the image features of stock data, considering the picture features of stock data and the characteristics of CNN’s good at extracting picture features, the paper proposed a stock price trend prediction model CNN-M based on a Convolutional Neural Network. At the same time, based on the excellent image feature extraction ability of the residual network, this paper proposed a residual network-based stock price trend prediction model ResNet-M based on the Conventional Neural Network. The experimental results showed that the prediction ability of the improved residual network-based prediction model Resnet-M is superior to the CNN model.

scholarly journals Short-Term Stock Price-Trend Prediction Using Meta-Learning

2021 ◽  
Author(s):  
Shin-Hung Chang ◽  
Cheng-Wen Hsu ◽  
Hsing-Ying Li ◽  
Wei-Sheng Zeng ◽  
Jan-Ming Ho
Keyword(s):  
Stock Price ◽  
Short Term ◽  
Trend Prediction ◽  
Price Trend ◽  
Meta Learning

Pressure Prediction in Natural Gas Desulfurization Process Based on PCA and SVR

2014 ◽  
Vol 962-965 ◽  
pp. 564-569 ◽  
Author(s):  
Yan Chao Shao ◽  
Liang Jun Xu ◽  
Yan Zhu Hu ◽  
Xin Bo Ai
Keyword(s):  
Natural Gas ◽  
Prediction Model ◽  
Measured Data ◽  
Support Vector ◽  
Trend Prediction ◽  
Generalization Ability ◽  
Model Based ◽  
Scada System ◽  
Desulfurization Process ◽  
Natural Gas Desulfurization

Pressure monitoring is an important means to reflect the running status of the natural gas desulphurization process. By using the data mining technology, the interaction relationships between the pressure and other monitoring parameters are analyzed in this paper. A pressure trend prediction model is established to show the pressure status in the natural gas desulfurization process. Firstly, the theory of Principal Component Analysis (PCA) is used to reduce the dimensions of measured data from traditional Supervisory Control and Data Acquisition (SCADA) system. Secondly the principal components are taken as input data into the pressure trend prediction model based on multiple regression theory of Support Vector Regression (SVR). Finally the accuracy and the generalization ability of the model are tested by the measured data obtained from SCADA system. Compared with other prediction models, pressure trend prediction model based on PCA and SVR gets smaller MSE and higher correlation. The pressure trend prediction model gets better generalization ability and stronger robustness, and is an effective complement to SCADA system in the natural gas desulphurization process.

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