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Which of the various algorithms is most effective at predicting a particular company’s stock price can vary depending on the situation. However, many researchers are currently using one deep learning algorithm, Long Short-Term Memory (LSTM), to predict stock prices with great success.
LSTM algorithm
LSTM is a type of recurrent neural network (RNN) that can learn long-term dependencies from sequential data, such as time series data. LSTMs are known to be powerful models that can remember past data and use it to predict the future.
Advantages of LSTMs
LSTMs are designed to solve the problem of long-term dependence, which is useful for identifying patterns in past data and predicting the price at the next point in time. They can also capture the temporal characteristics of the data to improve the accuracy of the prediction.
Uses of LSTMs
LSTMs are used to predict the price of a stock at the next point in time by taking time series data, such as historical price data or volume data, as input. Users can build and train an LSTM model to fit the characteristics of each company to create a predictive model.
Performance of LSTMs
Many studies have shown that LSTMs outperform other algorithms.
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