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Long Short-Term Memory (LSTM) is a type of Recurrent Neural Network (RNN) architecture that is particularly well suited for sequence prediction and time series forecasting tasks. The project objective encompasses the development and fine-tuning of LSTM models, with historical price data as the key input. The model is designed to anticipate the index price fluctuations within the predefined time interval. The major outcome of this project is to yield LSTM models with enhanced predictive accuracy, potentially revolutionizing price forecasting strategies. The derived insights will contribute to the evolving landscape of financial analytics, also empowers the traders with valuable insights for making more informed trading strategy decisions. This project can be considered under the DIGITAL BUSINESS section of INDUSTRY 4.0.

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Prediction of NIFTY 50 (FMCG) Index Prices Using Long Short-Term Memory Neural Networks

  • 会议论文

  • 发表日期：

    2026年

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  • Abhishek Pradeep,
  • S. Bharath Krishna,
  • Dhanush Gopan,
  • Rithika S. Raj,
  • V. Regi Kumar

摘要

Long Short-Term Memory (LSTM) is a type of Recurrent Neural Network (RNN) architecture that is particularly well suited for sequence prediction and time series forecasting tasks. The project objective encompasses the development and fine-tuning of LSTM models, with historical price data as the key input. The model is designed to anticipate the index price fluctuations within the predefined time interval. The major outcome of this project is to yield LSTM models with enhanced predictive accuracy, potentially revolutionizing price forecasting strategies. The derived insights will contribute to the evolving landscape of financial analytics, also empowers the traders with valuable insights for making more informed trading strategy decisions. This project can be considered under the DIGITAL BUSINESS section of INDUSTRY 4.0.