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Adaboost-Based Long Short-Term Memory Ensemble Learning Approach for Financial Time Series Forecasting
16 references·1 citing·49 second hop
Adaboost-Based Long Short-Term Memory Ensemble Learning Approach for Financial Time Series Forecasting (2018)Adaboost-Based Long Short-Term Memory Ens…Cointegration, error correction and exchange rate forecasting (2016)Cointegration, error correcti…Neural networks performance in exchange rate prediction (2015)Neural networks performance i…Modeling, forecasting and trading the EUR exchange rates with hybrid rolling genetic algorithms—Support vector regression forecast combinations (2015)Modeling, forecasting and tra…Forecasting exchange rate using deep belief networks and conjugate gradient method (2015)Forecasting exchange rate usi…Forecasting foreign exchange rates with adaptive neural networks using radial-basis functions and Particle Swarm Optimization (2012)Forecasting foreign exchange …Forecasting and trading the EUR/USD exchange rate with stochastic Neural Network combination and time-varying leverage (2012)Forecasting and trading the E…Combining seasonal ARIMA models with computational intelligence techniques for time series forecasting (2012)Combining seasonal ARIMA mode…Forecasting exchange rate volatility using high-frequency data: Is the euro different? (2011)Forecasting exchange rate vol…Chaos-based support vector regressions for exchange rate forecasting (2010)Chaos-based support vector re…Forecasting exchange rates with a large Bayesian VAR (2009)Forecasting exchange rates wi…Multistage RBF neural network ensemble learning for exchange rates forecasting (2008)Multistage RBF neural network…Regression neural network for error correction in foreign exchange forecasting and trading (2003)Regression neural network for…Forecasting daily foreign exchange rates using genetically optimized neural networks (2002)Forecasting daily foreign exc…Neural network forecasting of the British Pound/US Dollar exchange rate (1998)Neural network forecasting of…The predictive ability of several models of exchange rate volatility (1995)The predictive ability of sev…Forecasting exchange rates using feedforward and recurrent neural networks (1995)Forecasting exchange rates us…Applications of deep learning in stock market prediction: Recent progress (2021)Applications of deep learning…The Out-of-Sample Failure of Empirical Exchange Rate Models: Sampling Error or Misspecification? 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References

  • 1995Forecasting exchange rates using feedforward and recurrent neural networks428 cited
  • 1995The predictive ability of several models of exchange rate volatility371 cited
  • 1998Neural network forecasting of the British Pound/US Dollar exchange rate287 cited
  • 2015Forecasting exchange rate using deep belief networks and conjugate gradient method234 cited
  • 2008Multistage RBF neural network ensemble learning for exchange rates forecasting179 cited
  • 2002Forecasting daily foreign exchange rates using genetically optimized neural networks172 cited
  • 2009Forecasting exchange rates with a large Bayesian VAR169 cited
  • 2012Forecasting foreign exchange rates with adaptive neural networks using radial-basis functions and Particle Swarm Optimization167 cited
  • 2015Neural networks performance in exchange rate prediction160 cited
  • 2003Regression neural network for error correction in foreign exchange forecasting and trading151 cited
  • 2010Chaos-based support vector regressions for exchange rate forecasting119 cited
  • 2015Modeling, forecasting and trading the EUR exchange rates with hybrid rolling genetic algorithms—Support vector regression forecast combinations104 cited
  • 2012Combining seasonal ARIMA models with computational intelligence techniques for time series forecasting103 cited
  • 2012Forecasting and trading the EUR/USD exchange rate with stochastic Neural Network combination and time-varying leverage

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95 cited
  • 2011Forecasting exchange rate volatility using high-frequency data: Is the euro different?80 cited
  • 2016Cointegration, error correction and exchange rate forecasting33 cited
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    • 2021Applications of deep learning in stock market prediction: Recent progress774 cited