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Study on the prediction of stock price based on the associated network model of LSTM

1 references·16 citing·55 second hopsampled

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References

2010Forecasting stock markets using wavelet transforms and recurrent neural networks: An integrated system based on artificial bee colony algorithm399 cited

Cited by

2021Prediction of stock price direction using a hybrid GA-XGBoost algorithm with a three-stage feature engineering process292 cited
2021The applications of artificial neural networks, support vector machines, and long–short term memory for stock market prediction261 cited
2020A hybrid stock price index forecasting model based on variational mode decomposition and LSTM network191 cited
2020Deep Attentive Learning for Stock Movement Prediction From Social Media Text and Company Correlations146 cited
2022A comprehensive review on multiple hybrid deep learning approaches for stock prediction136 cited
2023Prediction of Complex Stock Market Data Using an Improved Hybrid EMD-LSTM Model133 cited
2022A stock price prediction method based on meta-learning and variational mode decomposition109 cited
2023A Stock Price Prediction Model Based on Investor Sentiment and Optimized Deep Learning100 cited
2020Predicting stock price trends based on financial news articles and using a novel twin support vector machine with fuzzy hyperplane96 cited
2022Interpretable stock price forecasting model using genetic algorithm-machine learning regressions and best feature subset selection78 cited
2020Spatiotemporal Hypergraph Convolution Network for Stock Movement Forecasting77 cited

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2023

A Systematic Survey of AI Models in Financial Market Forecasting for Profitability Analysis

76 cited

2021Stock Price Forecast Based on CNN-BiLSTM-ECA Model73 cited

2022An adaptive particle swarm optimization-based hybrid long short-term memory model for stock price time series forecasting67 cited

2023Attention-based CNN–LSTM for high-frequency multiple cryptocurrency trend prediction64 cited

2023Deep Learning for Financial Time Series Prediction: A State-of-the-Art Review of Standalone and Hybrid Models63 cited