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Forecast of LSTM-XGBoost in Stock Price Based on Bayesian Optimization
9 references·1 citing·44 second hop
Forecast of LSTM-XGBoost in Stock Price Based on Bayesian Optimization (2021)Forecast of LSTM-XGBoost in Stock Price B…Forecasting stock prices with long-short term memory neural network based on attention mechanism (2020)Forecasting stock prices with…LSTM Model Optimization on Stock Price Forecasting (2018)LSTM Model Optimization on St…Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models (2018)Forecasting the volatility of…Financial Trading Model with Stock Bar Chart Image Time Series with Deep Convolutional Neural Networks (2018)Financial Trading Model with …Stock price prediction using LSTM, RNN and CNN-sliding window model (2017)Stock price prediction using …Support vector regression with chaos-based firefly algorithm for stock market price forecasting (2012)Support vector regression wit…A multiple-kernel support vector regression approach for stock market price forecasting (2010)A multiple-kernel support vec…A hybrid ARIMA and support vector machines model in stock price forecasting (2004)A hybrid ARIMA and support ve…Are Stock Returns Predictable? A Test Using Markov Chains (1991)Are Stock Returns Predictable…A hybrid TCN-XGBoost model for agricultural product market price forecasting (2025)A hybrid TCN-XGBoost model fo…Time Series Analysis Forecasting and Control. (1971)Book Review: Time Series Analysis: Forecasting and Control (1977)Time series forecasting using a hybrid ARIMA and neural network model (2003)Surveying stock market forecasting techniques – Part II: Soft computing methods (2008)Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange (2010)Stock Price Prediction Using the ARIMA Model (2014)Time Series Analysis: Forecasting and Control. (1978)Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation (1982)Generalized autoregressive conditional heteroskedasticity (1986)Predicting stock market index using fusion of machine learning techniques (2014)Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies (2017)Financial time series forecasting model based on CEEMDAN and LSTM (2018)Efficient Capital Markets: A Review of Theory and Empirical Work (1970)EFFICIENT CAPITAL MARKETS: A REVIEW OF THEORY AND EMPIRICAL WORK* (1970)A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle (1989)Regime switching in stock market returns (1997)Nonlinearity and intraday efficiency tests on energy futures markets (2009)Nonlinearity, data-snooping, and stock index ETF return predictability (2009)25 years of time series forecasting (2006)Stock Closing Price Prediction using Machine Learning Techniques (2020)Applications of deep learning in stock market prediction: Recent progress (2021)Generación de series de tiempo financieras sintéticas para "data augmentation" usando redes neuronales generativas adversarias (GAN) (2021)Distribution of the Estimators for Autoregressive Time Series With a Unit Root (1979)A CNN-LSTM-Based Model to Forecast Stock Prices (2020)A Survey of Forex and Stock Price Prediction Using Deep Learning (2021)Forecasting stock indices with back propagation neural network (2011)Deep learning with long short-term memory networks for financial market predictions (2017)Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach (2018)Deep learning for financial applications : A survey (2020)Dividend yields and expected stock returns (1988)A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem (2017)Optimizing LSTM Based Network For Forecasting Stock Market (2020)Stock Market Trends Analysis using Extreme Gradient Boosting (XGBoost) (2023)A survey on uncertainty quantification in deep learning for financial time series prediction (2024)Application of support vector machines in financial time series forecasting (2001)Stock closing price prediction based on sentiment analysis and LSTM (2019)The applications of artificial neural networks, support vector machines, and long–short term memory for stock market prediction (2021)Predicting stock market index using LSTM (2022)Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications (2023)Forecasting method of stock market volatility in time series data based on mixed model of ARIMA and XGBoost (2020)A graph-based CNN-LSTM stock price prediction algorithm with leading indicators (2021)CNN-XGBoost fusion-based affective state recognition using EEG spectrogram image analysis (2022)A systematic review of fundamental and technical analysis of stock market predictions (2019)Machine learning techniques and data for stock market forecasting: A literature review (2022)
this work references cited by second hop

References

  • 2017Stock price prediction using LSTM, RNN and CNN-sliding window model1,134 cited
  • 2004A hybrid ARIMA and support vector machines model in stock price forecasting1,019 cited
  • 2018Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models807 cited
  • 2012Support vector regression with chaos-based firefly algorithm for stock market price forecasting471 cited
  • 2020Forecasting stock prices with long-short term memory neural network based on attention mechanism403 cited
  • 2010A multiple-kernel support vector regression approach for stock market price forecasting255 cited
  • 1991Are Stock Returns Predictable? A Test Using Markov Chains117 cited
  • 2018Financial Trading Model with Stock Bar Chart Image Time Series with Deep Convolutional Neural Networks59 cited
  • 2018LSTM Model Optimization on Stock Price Forecasting48 cited

Cited by

  • 2025A hybrid TCN-XGBoost model for agricultural product market price forecasting28 cited

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