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A Hybrid LMD–ARIMA–Machine Learning Framework for Enhanced Forecasting of Financial Time Series: Evidence from the NASDAQ Composite Index
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References

  • 1979Distribution of the Estimators for Autoregressive Time Series with a Unit Root23,183 cited
  • 2017Deep learning with long short-term memory networks for financial market predictions2,827 cited
  • 2017A deep learning framework for financial time series using stacked autoencoders and long-short term memory1,131 cited
  • 2017Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies833 cited
  • 2016Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500749 cited
  • 2014Predicting stock market index using fusion of machine learning techniques608 cited
  • 2019Universal features of price formation in financial markets: perspectives from deep learning267 cited
  • 2006A hybrid approach based on neural networks and genetic algorithms for detecting temporal patterns in stock markets223 cited
  • 2004Institutional trading and stock returns151 cited
  • 2022Multi-scale systemic risk and spillover networks of commodity markets in the bullish and bearish regimes138 cited
  • 2024Green bond issuance and green innovation: Evidence from China's energy industry136 cited
  • 2023A Deep Neural Network-Based Assistive Decision Method for Financial Risk Prediction in Carbon Trading Market135 cited
  • 2025Can green finance policy promote inclusive green growth?- Based on the quasi-natural experiment of China's green finance reform and innovation pilot zone

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131 cited
  • 2023Functional-Coefficient Quantile Regression for Panel Data with Latent Group Structure96 cited
  • 2023Multisource information fusion considering the weight of focal element’s beliefs: a Gaussian kernel similarity approach70 cited
  • 2023Forecasting Day-Ahead Brent Crude Oil Prices Using Hybrid Combinations of Time Series Models62 cited
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