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Long Short-Term Memory Network (LSTM) based Stock Price Prediction

Published: 29 August 2023 Publication History

Abstract

Predicting stock prices is a challenging and highly sought-after task in financial markets. In recent years, deep learning techniques, particularly Long Short-Term Memory (LSTM) networks, have shown promising results in capturing complex temporal dependencies and forecasting time series data. This research paper presents a LSTM-based framework for stock price prediction. The proposed framework utilizes historical stock price data. The LSTM model is designed to learn the underlying patterns and trends in the data, enabling it to make accurate predictions of future stock prices. We preprocess the data, including normalization and feature engineering, to enhance the model's ability to extract meaningful patterns. We employ appropriate evaluation metrics, such as mean squared error (MSE) and root mean squared error (RMSE), to assess the accuracy of the predictions. Experimental results demonstrate that the LSTM-based framework achieves competitive performance in stock price prediction compared to traditional statistical models and other machine learning approaches.

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Cited By

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  • (2024)Web Semantic Analysis of Investor Sentiment, Short Trading, and Stock Market VolatilityInternational Journal on Semantic Web & Information Systems10.4018/IJSWIS.35390320:1(1-35)Online publication date: 17-Sep-2024
  • (2024)Semantic Web Insights Into the Classification of Folk Paper-Cut Cultural GenesInternational Journal on Semantic Web & Information Systems10.4018/IJSWIS.35026620:1(1-15)Online publication date: 17-Sep-2024
  • (2024)Predicting Stock Trends Using Web Semantics and Feature FusionInternational Journal on Semantic Web & Information Systems10.4018/IJSWIS.34637820:1(1-24)Online publication date: 30-Jul-2024

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cover image ACM Conferences
RACS '23: Proceedings of the 2023 International Conference on Research in Adaptive and Convergent Systems
August 2023
251 pages
ISBN:9798400702280
DOI:10.1145/3599957
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 29 August 2023

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Author Tags

  1. LSTM
  2. Stock Price
  3. financial markets

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Overall Acceptance Rate 393 of 1,581 submissions, 25%

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Cited By

View all
  • (2024)Web Semantic Analysis of Investor Sentiment, Short Trading, and Stock Market VolatilityInternational Journal on Semantic Web & Information Systems10.4018/IJSWIS.35390320:1(1-35)Online publication date: 17-Sep-2024
  • (2024)Semantic Web Insights Into the Classification of Folk Paper-Cut Cultural GenesInternational Journal on Semantic Web & Information Systems10.4018/IJSWIS.35026620:1(1-15)Online publication date: 17-Sep-2024
  • (2024)Predicting Stock Trends Using Web Semantics and Feature FusionInternational Journal on Semantic Web & Information Systems10.4018/IJSWIS.34637820:1(1-24)Online publication date: 30-Jul-2024

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