Abstract
In recent years, numerous researchers across the world have developed various methods for predicting stock prices. However, the accuracy of these models has been found to be inconsistent. This field, known as stock market prediction and analysis, offers potential for further improvement. This paper proposes a framework based on a long short-term memory (LSTM)-based deep learning model, capable of accurately predicting the closing prices of companies listed on the National Stock Exchange (NSE) or Bombay Stock Exchange (BSE) of India. The LSTM model was trained using historical stock market data of Tata Motors and demonstrated a high degree of accuracy in predicting future price movements. The LSTM approach was found to be superior to other methods in terms of accuracy and precision.
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Rathi, H., Joardar, I., Dhanuka, G., Gupta, L., Angel Arul Jothi, J. (2023). Indian Stock Price Prediction Using Long Short-Term Memory. In: Bhateja, V., Yang, XS., Ferreira, M.C., Sengar, S.S., Travieso-Gonzalez, C.M. (eds) Evolution in Computational Intelligence. FICTA 2023. Smart Innovation, Systems and Technologies, vol 370. Springer, Singapore. https://doi.org/10.1007/978-981-99-6702-5_13
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DOI: https://doi.org/10.1007/978-981-99-6702-5_13
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