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This study introduces a novel Semi-Decentralized Long Short-Term Memory (SD-LSTM) architecture and compares its performance against a traditional LSTM model for stock price prediction, examining both accuracy and training time. All experiments employ canonical settings. Results indicate that SD-LSTM consistently achieves better prediction accuracy—evidenced by significantly lower mean squared error—across stock data from 5 major U.S. companies (Apple, NVIDIA, Amazon, Alphabet, Microsoft). Moreover, SD-LSTM accomplishes these improvements with fewer parameters. In terms of training speed, SD-LSTM is substantially faster than traditional LSTM when handling larger datasets and more complex configurations, highlighting its efficiency in parallel processing. Overall, these findings underscore the potential of this new SD-LSTM architecture for large-scale applications and its viability for integration into both established and emerging hybrid approaches that demand advanced predictive accuracy and computational efficiency.

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SD-LSTM: A Novel Semi–decentralized LSTM Architecture for Scalable and Accurate Stock Price Prediction

  • 会议论文

  • 发表日期：

    2026年

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  • Peng Li,
  • Roman Senkerik,
  • Zuzana Kominkova Oplatkova

摘要

This study introduces a novel Semi-Decentralized Long Short-Term Memory (SD-LSTM) architecture and compares its performance against a traditional LSTM model for stock price prediction, examining both accuracy and training time. All experiments employ canonical settings. Results indicate that SD-LSTM consistently achieves better prediction accuracy—evidenced by significantly lower mean squared error—across stock data from 5 major U.S. companies (Apple, NVIDIA, Amazon, Alphabet, Microsoft). Moreover, SD-LSTM accomplishes these improvements with fewer parameters. In terms of training speed, SD-LSTM is substantially faster than traditional LSTM when handling larger datasets and more complex configurations, highlighting its efficiency in parallel processing. Overall, these findings underscore the potential of this new SD-LSTM architecture for large-scale applications and its viability for integration into both established and emerging hybrid approaches that demand advanced predictive accuracy and computational efficiency.