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Stock price prediction based on the long short-term memory network
- 1 Green River College
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Abstract
Stock analysis is a challenging task that involves modelling complex and nonlinear dynamics of stock prices and volumes. Long Short-Term Memory (LSTM) is a type of recurrent neural network that can capture long-term dependencies and temporal patterns in time series data. In this paper, a stock analysis method based on LSTM is proposed that can predict future stock prices and transactions using historical data. Yfinance is used to obtain stock data of four technology companies (i.e. Apple, Google, Microsoft, and Amazon) and apply LSTM to extract features and forecast trends. Various techniques are also used such as moving average, correlation analysis, and risk assessment to evaluate the performance and risk of different stocks. When compare the method in this paper with other neural network models such as RNN and GRU, the result show that LSTM achieves better accuracy and stability in stock prediction. This paper demonstrates the effectiveness and applicability of LSTM method through experiments on real-world data sets.
Keywords
machine learning, stock price prediction, LSTM
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Cite this article
Zhang,S. (2023). Stock price prediction based on the long short-term memory network. Applied and Computational Engineering,18,28-32.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 5th International Conference on Computing and Data Science
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