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StockNet—GRU based stock index prediction
16 references·6 citing·40 second hop
StockNet—GRU based stock index prediction (2022)StockNet—GRU based stock index predictionForecasting stock prices with a feature fusion LSTM-CNN model using different representations of the same data (2019)Forecasting stock prices with…CNNpred: CNN-based stock market prediction using a diverse set of variables (2019)CNNpred: CNN-based stock mark…NSE Stock Market Prediction Using Deep-Learning Models (2018)NSE Stock Market Prediction U…ModAugNet: A new forecasting framework for stock market index value with an overfitting prevention LSTM module and a prediction LSTM module (2018)ModAugNet: A new forecasting …An innovative neural network approach for stock market prediction (2018)An innovative neural network …Stock price prediction using LSTM, RNN and CNN-sliding window model (2017)Stock price prediction using …Deep learning with long short-term memory networks for financial market predictions (2017)Deep learning with long short…Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies (2017)Deep learning networks for st…A deep learning framework for financial time series using stacked autoencoders and long-short term memory (2017)A deep learning framework for…Deep Direct Reinforcement Learning for Financial Signal Representation and Trading (2016)Deep Direct Reinforcement Lea…Deep learning for event-driven stock prediction (2015)Deep learning for event-drive…Predicting stock market index using fusion of machine learning techniques (2014)Predicting stock market index…Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques (2014)Predicting stock and stock pr…Using artificial neural network models in stock market index prediction (2011)Using artificial neural netwo…SMOOTH TRANSITION AUTOREGRESSIVE MODELS — A SURVEY OF RECENT DEVELOPMENTS (2002)SMOOTH TRANSITION AUTOREGRESS…Time Series Analysis, Forecasting and Control. (1971)Time Series Analysis, Forecas…Forecasting and analyzing technology development trends with self-attention and frequency enhanced LSTM (2024)Forecasting and analyzing tec…Data-driven stock forecasting models based on neural networks: A review (2024)Data-driven stock forecasting…Stock ranking prediction using a graph aggregation network based on stock price and stock relationship information (2023)Stock ranking prediction usin…Stock index forecasting based on multivariate empirical mode decomposition and temporal convolutional networks (2023)Stock index forecasting based…SMP-DL: a novel stock market prediction approach based on deep learning for effective trend forecasting (2023)SMP-DL: a novel stock market …A novel hybrid model for stock price forecasting integrating Encoder Forest and Informer (2023)A novel hybrid model for stoc…EFFICIENT CAPITAL MARKETS: A REVIEW OF THEORY AND EMPIRICAL WORK* (1970)Financial time series forecasting using support vector machines (2003)Forecasting stock market movement direction with support vector machine (2004)Evaluating multiple classifiers for stock price direction prediction (2015)Sentiment analysis on social media for stock movement prediction (2015)Time Series Analysis Forecasting and Control. 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(1985)Hierarchical structure in financial markets (1999)Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average? (2007)A systematic review of fundamental and technical analysis of stock market predictions (2019)Efficient Capital Markets: A Review of Theory and Empirical Work (1970)On Persistence in Mutual Fund Performance (1997)A Comparison of ARIMA and LSTM in Forecasting Time Series (2018)The Performance of LSTM and BiLSTM in Forecasting Time Series (2019)Generación de series de tiempo financieras sintéticas para "data augmentation" usando redes neuronales generativas adversarias (GAN) (2021)25 years of time series forecasting (2006)Stock Closing Price Prediction using Machine Learning Techniques (2020)Temporal Relational Ranking for Stock Prediction (2019)Long memory relationships and the aggregation of dynamic models (1980)Understanding spurious regressions in econometrics (1986)
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References

  • 2017Deep learning with long short-term memory networks for financial market predictions2,827 cited
  • 1971Time Series Analysis, Forecasting and Control.2,771 cited
  • 2002SMOOTH TRANSITION AUTOREGRESSIVE MODELS — A SURVEY OF RECENT DEVELOPMENTS1,175 cited
  • 2014Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques1,139 cited
  • 2017A deep learning framework for financial time series using stacked autoencoders and long-short term memory1,131 cited
  • 2017Stock price prediction using LSTM, RNN and CNN-sliding window model1,096 cited
  • 2017Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies833 cited
  • 2016Deep Direct Reinforcement Learning for Financial Signal Representation and Trading821 cited
  • 2011Using artificial neural network models in stock market index prediction787 cited
  • 2015Deep learning for event-driven stock prediction675 cited
  • 2019CNNpred: CNN-based stock market prediction using a diverse set of variables643 cited
  • 2018NSE Stock Market Prediction Using Deep-Learning Models615 cited
  • 2014Predicting stock market index using fusion of machine learning techniques608 cited
  • 2018An innovative neural network approach for stock market prediction

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476 cited
  • 2018ModAugNet: A new forecasting framework for stock market index value with an overfitting prevention LSTM module and a prediction LSTM module373 cited
  • 2019Forecasting stock prices with a feature fusion LSTM-CNN model using different representations of the same data342 cited
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    • 2023A novel hybrid model for stock price forecasting integrating Encoder Forest and Informer62 cited
    • 2023Stock ranking prediction using a graph aggregation network based on stock price and stock relationship information57 cited
    • 2024Forecasting and analyzing technology development trends with self-attention and frequency enhanced LSTM22 cited