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Forecasting stochastic neural network based on financial empirical mode decomposition
13 references·12 citing·50 second hop
Forecasting stochastic neural network based on financial empirical mode decomposition (2017)Forecasting stochastic neural network bas…Forecasting stock market indexes using principle component analysis and stochastic time effective neural networks (2015)Forecasting stock market inde…A hybrid modeling approach for forecasting the volatility of S&P 500 index return (2011)A hybrid modeling approach fo…A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems (2010)A comparative survey of artif…Forecasting model of global stock index by stochastic time effective neural network (2009)Forecasting model of global s…Short-term predictability of crude oil markets: A detrended fluctuation analysis approach (2008)Short-term predictability of …Forecasting the volatility of stock price index (2006)Forecasting the volatility of…Analysis of Financial Time Series (2006)Analysis of Financial Time Se…A dynamic artificial neural network model for forecasting time series events (2004)A dynamic artificial neural n…Neural network forecasts of Canadian stock returns using accounting ratios (2003)Neural network forecasts of C…A theory of power-law distributions in financial market fluctuations (2003)A theory of power-law distrib…Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index (2000)Genetic algorithms approach t…An Introduction to Econophysics: Correlations and Complexity in Finance (2000)An Introduction to Econophysi…Neural network forecasting of the British Pound/US Dollar exchange rate (1998)Neural network forecasting of…A Deep Learning Based Expert Framework for Portfolio Prediction and Forecasting (2024)A Deep Learning Based Expert …Stock index forecasting based on multivariate empirical mode decomposition and temporal convolutional networks (2023)Stock index forecasting based…Centralized decomposition approach in LSTM for Bitcoin price prediction (2023)Centralized decomposition app…A novel interval-valued carbon price analysis and forecasting system based on multi-objective ensemble strategy for carbon trading market (2023)A novel interval-valued carbo…Forecasting energy prices using a novel hybrid model with variational mode decomposition (2022)Forecasting energy prices usi…Asian stock markets closing index forecast based on secondary decomposition, multi-factor analysis and attention-based LSTM model (2022)Asian stock markets closing i…Forecasting Nickel futures price based on the empirical wavelet transform and gradient boosting decision trees (2021)Forecasting Nickel futures pr…Financial Forecasting with Machine Learning: Price Vs Return (2021)Financial Forecasting with Ma…Diversity-driven knowledge distillation for financial trading using Deep Reinforcement Learning (2021)Diversity-driven knowledge di…Blockchain financial investment based on deep learning network algorithm (2020)Blockchain financial investme…A new hybrid financial time series prediction model (2020)A new hybrid financial time s…A Hybrid Attention-Based EMD-LSTM Model for Financial Time Series Prediction (2019)A Hybrid Attention-Based EMD-…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)A hybrid model based on rough sets theory and genetic algorithms for stock price forecasting (2010)A hybrid ANFIS model based on empirical mode decomposition for stock time series forecasting (2016)Artificial Intelligence Applied to Stock Market Trading: A Review (2021)Fractional Differencing (1981)The Great Crash, the Oil Price Shock, and the Unit Root Hypothesis (1989)Multifractal detrended fluctuation analysis of nonstationary time series (2002)Analysis of efficiency for Shenzhen stock market based on multifractal detrended fluctuation analysis (2009)Cross-correlations between Chinese A-share and B-share markets (2010)An analysis of stock market efficiency: Developed vs Islamic stock markets using MF-DFA (2014)Time Series Analysis: Forecasting and Control (1977)Comparing Predictive Accuracy (1994)Comparison of direct and iterative artificial neural network forecast approaches in multi-periodic time series forecasting (2008)Forecasting daily stock trend using multi-filter feature selection and deep learning (2020)Financial statement analysis and the prediction of stock returns (1989)Artificial neural network models for forecasting and decision making (1994)How effective are neural networks at forecasting and prediction? 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References

  • 2006Analysis of Financial Time Series2,348 cited
  • 2000An Introduction to Econophysics: Correlations and Complexity in Finance2,342 cited
  • 2003A theory of power-law distributions in financial market fluctuations1,283 cited
  • 2000Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index758 cited
  • 2010A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems552 cited
  • 1998Neural network forecasting of the British Pound/US Dollar exchange rate287 cited
  • 2004A dynamic artificial neural network model for forecasting time series events257 cited
  • 2008Short-term predictability of crude oil markets: A detrended fluctuation analysis approach245 cited
  • 2006Forecasting the volatility of stock price index236 cited
  • 2003Neural network forecasts of Canadian stock returns using accounting ratios182 cited
  • 2015Forecasting stock market indexes using principle component analysis and stochastic time effective neural networks163 cited
  • 2009Forecasting model of global stock index by stochastic time effective neural network151 cited
  • 2011A hybrid modeling approach for forecasting the volatility of S&P 500 index return150 cited

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  • 2021

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Forecasting Nickel futures price based on the empirical wavelet transform and gradient boosting decision trees
94 cited
  • 2022Asian stock markets closing index forecast based on secondary decomposition, multi-factor analysis and attention-based LSTM model84 cited
  • 2020A new hybrid financial time series prediction model82 cited
  • 2023Stock index forecasting based on multivariate empirical mode decomposition and temporal convolutional networks69 cited
  • 2022Forecasting energy prices using a novel hybrid model with variational mode decomposition59 cited
  • 2021Financial Forecasting with Machine Learning: Price Vs Return54 cited
  • 2019A Hybrid Attention-Based EMD-LSTM Model for Financial Time Series Prediction53 cited
  • 2020Blockchain financial investment based on deep learning network algorithm53 cited
  • 2021Diversity-driven knowledge distillation for financial trading using Deep Reinforcement Learning46 cited
  • 2024A Deep Learning Based Expert Framework for Portfolio Prediction and Forecasting44 cited
  • 2023Centralized decomposition approach in LSTM for Bitcoin price prediction43 cited
  • 2023A novel interval-valued carbon price analysis and forecasting system based on multi-objective ensemble strategy for carbon trading market42 cited