检索结果-南通市图书馆

Research on Stock Price prediction Method Based on the GAN-LSTM-Attention Model

Computers, Materials & Continua 2025年第1期82卷 609-625页

作者： Peng Li Yanrui Wei Lili Yin College of Computer Science and Technology Harbin University of Science and TechnologyHarbin150006China

Stock price prediction is a typical complex time series prediction problem characterized by dynamics,nonlinearity,and *** paper introduces a generative adversarial network model that incorporates an attention mechanism(GAN-LSTM-Attention)to improve the accuracy of stock price ***,the generator of this model combines the Long and Short-Term Memory Network(LSTM),the Attention Mechanism and,the Fully-Connected Layer,focusing on generating the predicted stock *** discriminator combines the Convolutional Neural Network(CNN)and the Fully-Connected Layer to discriminate between real stock prices and generated stock ***,to evaluate the practical application ability and generalization ability of the GAN-LSTM-Attention model,four representative stocks in the United States of America(USA)stock market,namely,Standard&Poor’s 500 Index stock,Apple Incorporatedstock,AdvancedMicroDevices Incorporatedstock,and Google Incorporated stock were selected for prediction experiments,and the prediction performance was comprehensively evaluated by using the three evaluation metrics,namely,mean absolute error(MAE),root mean square error(RMSE),and coefficient of determination(R2).Finally,the specific effects of the attention mechanism,convolutional layer,and fully-connected layer on the prediction performance of the model are systematically analyzed through ablation *** results of experiment show that the GAN-LSTM-Attention model exhibits excellent performance and robustness in stock price prediction.

关键词： Stock price prediction generative adversarial network attention mechanism time-series prediction

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Pandemic Analysis and prediction of COVID-19 Using Gaussian Doubling times

引用

Computers, Materials & Continua 2022年第7期72卷 833-849页

作者： Saleh Albahli Farman Hassan Ali Javed Aun Irtaza Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computer Science University of Engineering and TechnologyTaxilaPakistan Department of Computer Science and Engineering Oakland UniversityMIUSA Department of Computer and Electrical Engineering University of MichiganDearbornUSA

COVID-19 has become a pandemic,with cases all over the world,with widespread disruption in some countries,such as Italy,US,India,South Korea,and *** and reliable detection of COVID-19 is mandatory to control the spread of ***,prediction of COVID-19 spread in near future is also crucial to better plan for the disease *** this purpose,we proposed a robust framework for the analysis,prediction,and detection of *** make reliable estimates on key pandemic parameters and make predictions on the point of inflection and possible washout time for various countries around the *** estimates,analysis and predictions are based on the data gathered fromJohns Hopkins Center during the time span of April 21 to June 27,*** use the normal distribution for simple and quick predictions of the coronavirus pandemic model and estimate the parameters of Gaussian curves using the least square parameter curve fitting for several countries in different *** predictions rely on the possible outcomes of Gaussian time evolution with the central limit theorem of statistics the predictions to be well *** parameters of Gaussian distribution,i.e.,maximumtime and width,are determined through a statisticalχ^(2)-fit for the purpose of doubling times after April 21,*** COVID-19 detection,we proposed a novel method based on the Histogram of Oriented Gradients(HOG)and CNN in multi-class classification scenario i.e.,Normal,COVID-19,viral pneumonia *** results show the effectiveness of our framework for reliable prediction and detection of COVID-19.

关键词： prediction coronavirus time-series prediction gaussian mathematical model pandemic spreading

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Deep learning for time series forecasting:The electric load case

引用

CAAI Transactions on Intelligence Technology 2022年第1期7卷 1-25页

作者： Alberto Gasparin Slobodan Lukovic Cesare Alippi Faculty of Informatics Universitàdella Svizzera ItalianaLuganoSwitzerland Department of Electronics Informationand BioengineeringPolitecnico di MilanoMilanItaly

Management and efficient operations in critical infrastructures such as smart grids take huge advantage of accurate power load forecasting,which,due to its non-linear nature,remains a challenging ***,deep learning has emerged in the machine learning field achieving impressive performance in a vast range of tasks,from image classification to machine *** of deep learning models to the electric load forecasting problem are gaining interest among researchers as well as the industry,but a comprehensive and sound comparison among different-also traditional-architectures is not yet available in the *** work aims at filling the gap by reviewing and experimentally evaluating four real world datasets on the most recent trends in electric load forecasting,by contrasting deep learning architectures on short-term forecast(oneday-ahead prediction).Specifically,the focus is on feedforward and recurrent neural networks,sequence-to-sequence models and temporal convolutional neural networks along with architectural variants,which are known in the signal processing community but are novel to the load forecasting one.

关键词： deep learning electric load forecasting multi-step ahead forecasting smart grid time-series prediction

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Sensory Data prediction Using Spatiotemporal Correlation and LSTM Recurrent Neural Network

引用

Instrumentation 2019年第3期6卷 10-17页

作者： Tongxin SHU Department of Electrical and Computer Engineering The University of British ColumbiaVancouverCanada V6T 1Z4

The Wireless Sensor Networks(WSNs)are widely utilized in various industrial and environmental monitoring *** process of data gathering within the WSN is significant in terms of reporting the environmental ***,it might occur that certain sensor node malfunctions due to the energy draining out or unexpected ***,the collected data may become inaccurate or *** on the spatiotemporal correlation among sensor nodes,this paper proposes a novel algorithm to predict the value of the missing or inaccurate data and predict the future data in replacement of certain nonfunctional sensor *** Long-Short-Term-Memory Recurrent Neural Network(LSTM RNN)helps to more accurately derive the time-series data corresponding to the sets of past collected data,making the prediction results more *** is observed from the simulation results that the proposed algorithm provides an outstanding data gathering efficiency while ensuring the data accuracy.

关键词： Spatiotemporal correlation LSTM Recurrent Neural Network time-series prediction

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：