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A new decomposition ensemble model for stock price forecasting based on system clustering and particle swarm optimization
16 references·1 citing·46 second hopsampled
A new decomposition ensemble model for stock price forecasting based on system clustering and particle swarm optimization (2022)A new decomposition ensemble model for st…Forecasting gold price using a novel hybrid model with ICEEMDAN and LSTM-CNN-CBAM (2022)Forecasting gold price using …The two-stage machine learning ensemble models for stock price prediction by combining mode decomposition, extreme learning machine and improved harmony search algorithm (2020)The two-stage machine learnin…The application research of neural network and BP algorithm in stock price pattern classification and prediction (2020)The application research of n…Stock price prediction using deep learning and frequency decomposition (2020)Stock price prediction using …A new financial data forecasting model using genetic algorithm and long short-term memory network (2020)A new financial data forecast…A hybrid stock price index forecasting model based on variational mode decomposition and LSTM network (2020)A hybrid stock price index fo…Predicting monthly biofuel production using a hybrid ensemble forecasting methodology (2019)Predicting monthly biofuel pr…Potential Dependence of Financial Cycles between Emerging and Developed Countries: Based on ARIMA-GARCH Copula Model (2019)Potential Dependence of Finan…LSTM with Wavelet Transform Based Data Preprocessing for Stock Price Prediction (2019)LSTM with Wavelet Transform B…Hybrid Variational Mode Decomposition and evolutionary robust kernel extreme learning machine for stock price and movement prediction on daily basis (2018)Hybrid Variational Mode Decom…Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models (2018)Forecasting the volatility of…An Adaptive SVR for High-Frequency Stock Price Forecasting (2018)An Adaptive SVR for High-Freq…Forecasting price movements using technical indicators: Investigating the impact of varying input window length (2017)Forecasting price movements u…A hybrid ANFIS model based on empirical mode decomposition for stock time series forecasting (2016)A hybrid ANFIS model based on…A decomposition–ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting (2015)A decomposition–ensemble mode…A hybrid modeling approach for forecasting the volatility of S&P 500 index return (2011)A hybrid modeling approach fo…McVCsB: A new hybrid deep learning network for stock index prediction (2023)McVCsB: A new hybrid deep lea…Comparing Predictive Accuracy (1994)Use of Cumulative Sums of Squares for Retrospective Detection of Changes of Variance (1994)Comparing Predictive Accuracy (1995)A multiscale analysis for carbon price drivers (2018)Carbon price forecasting with variational mode decomposition and optimal combined model (2018)Oil price forecasting using a hybrid model (2018)Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation (1982)Generalized autoregressive conditional heteroskedasticity (1986)Conditional Heteroskedasticity in Asset Returns: A New Approach (1991)Global stock market investment strategies based on financial network indicators using machine learning techniques (2018)Stock market forecasting with super-high dimensional time-series data using ConvLSTM, trend sampling, and specialized data augmentation (2020)A hybrid approach of adaptive wavelet transform, long short-term memory and ARIMA-GARCH family models for the stock index prediction (2021)Time Series Analysis: Forecasting and Control (1977)Cascading logistic regression onto gradient boosted decision trees for forecasting and trading stock indices (2019)Designing fuzzy time series forecasting models: A survey (2019)Forecasting stock index price using the CEEMDAN-LSTM model (2021)The Behavior of Stock-Market Prices (1965)Mutual Fund Performance (1966)Financial time series forecasting using support vector machines (2003)A novel data-driven stock price trend prediction system (2017)Combining wavelet decomposition with machine learning to forecast gold returns (2019)Deep Learning-based Integrated Framework for stock price movement prediction (2022)Distribution of the Estimators for Autoregressive Time Series with a Unit Root (1979)A CNN-LSTM-Based Model to Forecast Stock Prices (2020)Forecasting stock prices with long-short term memory neural network based on attention mechanism (2020)A Survey of Forex and Stock Price Prediction Using Deep Learning (2021)Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange (2010)Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques (2014)Point and interval forecasting for carbon price based on an improved analysis-forecast system (2019)Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm (2008)Financial time series forecasting model based on CEEMDAN and LSTM (2018)A stock price prediction method based on meta-learning and variational mode decomposition (2022)Multi-step-ahead stock price index forecasting using long short-term memory model with multivariate empirical mode decomposition (2022)Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020–2022 (2023)Investor Relations and Information Assimilation (2018)Is Sell‐Side Research More Valuable in Bad Times? (2018)Pearson Correlation Coefficient-Based Performance Enhancement of Broad Learning System for Stock Price Prediction (2022)Prediction of stock market index based on ISSA-BP neural network (2022)Efficient Capital Markets: A Review of Theory and Empirical Work (1970)EFFICIENT CAPITAL MARKETS: A REVIEW OF THEORY AND EMPIRICAL WORK* (1970)The Performance of LSTM and BiLSTM in Forecasting Time Series (2019)Stock price prediction with optimized deep LSTM network with artificial rabbits optimization algorithm (2023)TRNN: An efficient time-series recurrent neural network for stock price prediction (2023)A forecast comparison of volatility models: does anything beat a GARCH(1,1)? (2005)A novel deep learning carbon price short-term prediction model with dual-stage attention mechanism (2023)Forecasting carbon price in China unified carbon market using a novel hybrid method with three-stage algorithm and long short-term memory neural networks (2023)
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References

  • 2018Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models786 cited
  • 2020Stock price prediction using deep learning and frequency decomposition373 cited
  • 2020The application research of neural network and BP algorithm in stock price pattern classification and prediction225 cited
  • 2022Forecasting gold price using a novel hybrid model with ICEEMDAN and LSTM-CNN-CBAM206 cited
  • 2015A decomposition–ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting204 cited
  • 2020A hybrid stock price index forecasting model based on variational mode decomposition and LSTM network191 cited
  • 2016A hybrid ANFIS model based on empirical mode decomposition for stock time series forecasting190 cited
  • 2017Forecasting price movements using technical indicators: Investigating the impact of varying input window length190 cited
  • 2018Hybrid Variational Mode Decomposition and evolutionary robust kernel extreme learning machine for stock price and movement prediction on daily basis156 cited
  • 2011A hybrid modeling approach for forecasting the volatility of S&P 500 index return150 cited
  • 2020A new financial data forecasting model using genetic algorithm and long short-term memory network127 cited
  • 2018An Adaptive SVR for High-Frequency Stock Price Forecasting108 cited
  • 2020The two-stage machine learning ensemble models for stock price prediction by combining mode decomposition, extreme learning machine and improved harmony search algorithm

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107 cited
  • 2019Predicting monthly biofuel production using a hybrid ensemble forecasting methodology100 cited
  • 2019LSTM with Wavelet Transform Based Data Preprocessing for Stock Price Prediction86 cited
  • 2019Potential Dependence of Financial Cycles between Emerging and Developed Countries: Based on ARIMA-GARCH Copula Model57 cited
  • Cited by

    • 2023McVCsB: A new hybrid deep learning network for stock index prediction45 cited